Journal ArticleDOI
Very high resolution interpolated climate surfaces for global land areas.
Robert J. Hijmans,Susan E. Cameron,Susan E. Cameron,Juan L. Parra,Peter G. Jones,Andy Jarvis +5 more
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In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).Abstract:
We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright 2005 Royal Meteorological Society.read more
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Journal ArticleDOI
Conservation assessment and prioritization of areas in Northeast India: Priorities for amphibians and reptiles
Samraat Pawar,Michelle S. Koo,Christopher D. Kelley,M. Firoz Ahmed,Sujoy Chaudhuri,Sahotra Sarkar +5 more
TL;DR: In this article, a two-step protocol was used to project species' potential geographic occurrences, and the resulting probabilistic distribution data were used to prioritize areas with algorithms that maximize the representation of all species in minimal total area.
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Systems dynamics and the spatial distribution of methane emissions from African domestic ruminants to 2030
Mario Herrero,Mario Herrero,Philip K. Thornton,Philip K. Thornton,Russell L. Kruska,Robin S. Reid +5 more
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Predicting the invasion risk by the alien bee-hawking Yellow-legged hornet Vespa velutina nigrithorax across Europe and other continents with niche models
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Profile or group discriminative techniques? Generating reliable species distribution models using pseudo‐absences and target‐group absences from natural history collections
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Evaluating mechanisms of diversification in a Guineo-Congolian tropical forest frog using demographic model selection
Daniel M. Portik,Adam D. Leaché,Danielle Rivera,Michael F. Barej,Marius Burger,Mareike Hirschfeld,Mark-Oliver Rödel,David C. Blackburn,Matthew K. Fujita +8 more
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References
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Journal ArticleDOI
An improved method of constructing a database of monthly climate observations and associated high-resolution grids
Timothy D. Mitchell,Philip Jones +1 more
TL;DR: In this paper, a database of monthly climate observations from meteorological stations is constructed and checked for inhomogeneities in the station records using an automated method that refines previous methods by using incomplete and partially overlapping records and by detecting inhomalities with opposite signs in different seasons.
Journal ArticleDOI
A high-resolution data set of surface climate over global land areas
TL;DR: In this paper, the construction of a 10' latitude/longitude data set of mean monthly sur-face climate over global land areas, excluding Antarctica, was described, which includes 8 climate conditions: precipitation, wet-day frequency, temperature, diurnal temperature range, relative humid-ity, sunshine duration, ground frost frequency and windspeed.
Journal ArticleDOI
Representing Twentieth-Century Space–Time Climate Variability. Part I: Development of a 1961–90 Mean Monthly Terrestrial Climatology
Mark New,Mike Hulme,Phil Jones +2 more
TL;DR: In this article, a 0.5° lat × 0. 5° long surface climatology of global land areas, excluding Antarctica, is described, which represents the period 1961-90 and comprises a suite of nine variables: precipitation, wet-day frequency, mean temperature, diurnal temperature range, vapor pressure, sunshine, cloud cover, ground frost frequency, and wind speed.
Journal ArticleDOI
Generating surfaces of daily meteorological variables over large regions of complex terrain
TL;DR: In this paper, a method for generating daily surfaces of temperature, precipitation, humidity, and radiation over large regions of complex terrain is presented, based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations.
Journal ArticleDOI
A knowledge-based approach to the statistical mapping of climate
TL;DR: In this article, the authors present a knowledge-based framework for climate mapping using a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model).
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