Very high resolution interpolated climate surfaces for global land areas.
TL;DR: 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.
Citations
More filters
TL;DR: Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%.
Abstract: This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.
2,228 citations
TL;DR: A new index of the velocity of temperature change (km yr-1), derived from spatial gradients and multimodel ensemble forecasts of rates of temperature increase in the twenty-first century, indicates management strategies for minimizing biodiversity loss from climate change.
Abstract: The ranges of plants and animals are moving in response to recent changes in climate. As temperatures rise, ecosystems with 'nowhere to go', such as mountains, are considered to be more threatened. However, species survival may depend as much on keeping pace with moving climates as the climate's ultimate persistence. Here we present a new index of the velocity of temperature change (km yr(-1)), derived from spatial gradients ( degrees C km(-1)) and multimodel ensemble forecasts of rates of temperature increase ( degrees C yr(-1)) in the twenty-first century. This index represents the instantaneous local velocity along Earth's surface needed to maintain constant temperatures, and has a global mean of 0.42 km yr(-1) (A1B emission scenario). Owing to topographic effects, the velocity of temperature change is lowest in mountainous biomes such as tropical and subtropical coniferous forests (0.08 km yr(-1)), temperate coniferous forest, and montane grasslands. Velocities are highest in flooded grasslands (1.26 km yr(-1)), mangroves and deserts. High velocities suggest that the climates of only 8% of global protected areas have residence times exceeding 100 years. Small protected areas exacerbate the problem in Mediterranean-type and temperate coniferous forest biomes. Large protected areas may mitigate the problem in desert biomes. These results indicate management strategies for minimizing biodiversity loss from climate change. Montane landscapes may effectively shelter many species into the next century. Elsewhere, reduced emissions, a much expanded network of protected areas, or efforts to increase species movement may be necessary.
2,014 citations
TL;DR: New methods for quantifying niche overlap that rely on a traditional ecological measure and a metric from mathematical statistics are developed and suggest various randomization tests that may prove useful in other areas of ecology and evolutionary biology.
Abstract: Environmental niche models, which are generated by combining species occurrence data with environmental GIS data layers, are increasingly used to answer fundamental questions about niche evolution, speciation, and the accumulation of ecological diversity within clades. The question of whether environmental niches are conserved over evolutionary time scales has attracted considerable attention, but often produced conflicting conclusions. This conflict, however, may result from differences in how niche similarity is measured and the specific null hypothesis being tested. We develop new methods for quantifying niche overlap that rely on a traditional ecological measure and a metric from mathematical statistics. We reexamine a classic study of niche conservatism between sister species in several groups of Mexican animals, and, for the first time, address alternative definitions of "niche conservatism" within a single framework using consistent methods. As expected, we find that environmental niches of sister species are more similar than expected under three distinct null hypotheses, but that they are rarely identical. We demonstrate how our measures can be used in phylogenetic comparative analyses by reexamining niche divergence in an adaptive radiation of Cuban anoles. Our results show that environmental niche overlap is closely tied to geographic overlap, but not to phylogenetic distances, suggesting that niche conservatism has not constrained local communities in this group to consist of closely related species. We suggest various randomization tests that may prove useful in other areas of ecology and evolutionary biology.
1,993 citations
TL;DR: In this article, the authors presented the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30'arc'sec.
Abstract: High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better. Machine-accessible metadata file describing the reported data (ISA-Tab format)
1,859 citations
Paul Sabatier University1, National Autonomous University of Mexico2, Carnegie Institution for Science3, University of São Paulo4, National University of Colombia5, Norwegian University of Life Sciences6, Amazon.com7, University of Leeds8, United Nations9, Smithsonian Tropical Research Institute10, University of Edinburgh11, Costa Rica Institute of Technology12
TL;DR: This work analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types, and found a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height.
Abstract: Terrestrial carbon stock mapping is important for the successful implementation of climate change mitigation policies. Its accuracy depends on the availability of reliable allometric models to infer oven-dry aboveground biomass of trees from census data. The degree of uncertainty associated with previously published pantropical aboveground biomass allometries is large. We analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types (4004 trees ≥ 5 cm trunk diameter). When trunk diameter, total tree height, and wood specific gravity were included in the aboveground biomass model as covariates, a single model was found to hold across tropical vegetation types, with no detectable effect of region or environmental factors. The mean percent bias and variance of this model was only slightly higher than that of locally fitted models. Wood specific gravity was an important predictor of aboveground biomass, especially when including a much broader range of vegetation types than previous studies. The generic tree diameter-height relationship depended linearly on a bioclimatic stress variable E, which compounds indices of temperature variability, precipitation variability, and drought intensity. For cases in which total tree height is unavailable for aboveground biomass estimation, a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height. However, to minimize bias, the development of locally derived diameter-height relationships is advised whenever possible. Both new allometric models should contribute to improve the accuracy of biomass assessment protocols in tropical vegetation types, and to advancing our understanding of architectural and evolutionary constraints on woody plant development.
1,750 citations
Cites background or methods from "Very high resolution interpolated c..."
...Because global climatic compilations have greatly improved over the past decade (Hijmans et al., 2005), it is now possible to obtain far better bioclimatic descriptors at ecological study sites than the ones derived from the Holdridge life zone system....
[...]
...Local climatic information was extracted from global gridded climatologies, which interpolate data from available meteorological stations (New et al., 2002; Hijmans et al., 2005)....
[...]
...Temperature and rainfall variables were acquired from the WorldClim database (Hijmans et al., 2005), which reports gridded mean climate values from the 1950–2000 period....
[...]
References
More filters
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.
Abstract: A database of monthly climate observations from meteorological stations is constructed. The database includes six climate elements and extends over the global land surface. The database is 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 inhomogeneities with opposite signs in different seasons. The method includes the development of reference series using neighbouring stations. Information from different sources about a single station may be combined, even without an overlapping period, using a reference series. Thus, a longer station record may be obtained and fragmentation of records reduced. The reference series also enables 1961–90 normals to be calculated for a larger proportion of stations. The station anomalies are interpolated onto a 0.5° grid covering the global land surface (excluding Antarctica) and combined with a published normal from 1961–90. Thus, climate grids are constructed for nine climate variables (temperature, diurnal temperature range, daily minimum and maximum temperatures, precipitation, wet-day frequency, frost-day frequency, vapour pressure, and cloud cover) for the period 1901–2002. This dataset is known as CRU TS 2.1 and is publicly available (http://www.cru.uea.ac.uk/). Copyright 2005 Royal Meteorological Society.
4,011 citations
"Very high resolution interpolated c..." refers background in this paper
...…we have made significant progress, additional efforts to compile and capture climate data are needed to improve spatial and temporal coverage of the available climate data and quality control (Mitchell and Jones, 2005), and interpolation methods can be further refined to better use these data....
[...]
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.
Abstract: We describe the construction of a 10' latitude/longitude data set of mean monthly sur- face climate over global land areas, excluding Antarctica The climatology includes 8 climate ele- ments —precipitation, wet-day frequency, temperature, diurnal temperature range, relative humid- ity, sunshine duration, ground frost frequency and windspeed—and was interpolated from a data set of station means for the period centred on 1961 to 1990 Precipitation was first defined in terms of the parameters of the Gamma distribution, enabling the calculation of monthly precipitation at any given return period The data are compared to an earlier data set at 05o latitude/longitude resolution and show added value over most regions The data will have many applications in applied climatology, biogeochemical modelling, hydrology and agricultural meteorology and are available through the International Water Management Institute World Water and Climate Atlas (http://wwwiwmiorg) and the Climatic Research Unit (http://wwwcruueaacuk)
2,206 citations
"Very high resolution interpolated c..." refers background or methods or result in this paper
...We aggregated the climate surfaces to a 10 arc min resolution to illustrate the benefits of higher resolution surfaces and to compare our results to those of New et al. (2002)....
[...]
...25: 1965–1978 (2005) resolution was chosen because it is the highest resolution global climate data set that was available before our study (New et al., 2002) and in order to compare with that data set....
[...]
...Within-grid cell variation in elevation was evaluated by mapping the range of elevations of the 3 arc s resolution grid cells within each 30 arc s cell....
[...]
...Other differences are related to the use of a different set of weather stations, and, no doubt, to some residual errors in our data set and that of New et al. (2002)....
[...]
...Our surfaces have a 30 arc s spatial resolution; this is equivalent to about 0.86 km2 at the equator and less elsewhere and commonly referred to as ‘1-km’ resolution....
[...]
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.
Abstract: The construction of a 0.5° lat × 0.5° long surface climatology of global land areas, excluding Antarctica, is described. The climatology 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. The climate surfaces have been constructed from a new dataset of station 1961–90 climatological normals, numbering between 19 800 (precipitation) and 3615 (wind speed). The station data were interpolated as a function of latitude, longitude, and elevation using thin-plate splines. The accuracy of the interpolations are assessed using cross validation and by comparison with other climatologies. This new climatology represents an advance over earlier published global terrestrial climatologies in that it is strictly constrained to the period 1961–90, describes an extended suite of surface climate variables, explicitly incorporates elevation...
1,880 citations
"Very high resolution interpolated c..." refers background or methods in this paper
...For many applications, data at a fine (≤1 km2) spatial resolution are necessary to capture environmental variability that can be partly lost at lower resolutions, particularly in mountainous and other areas with steep climate gradients....
[...]
...We chose this method because it has been used in other global studies (New et al., 1999, 2002), performed well in comparative tests of multiple interpolation techniques (Hartkamp et al., 1999; Jarvis and Stuart, 2001), and because it is computationally efficient and easy to run....
[...]
...Leemans and Cramer (1991) and New et al. (1999) created important earlier data sets, at a spatial resolution of 0.5° (55.6 km at the equator)....
[...]
...This database includes monthly mean (3084 stations), minimum and maximum (both 2504 stations) temperature and precipitation (4261 stations)....
[...]
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.
1,309 citations
"Very high resolution interpolated c..." refers background or methods in this paper
...…sets of high-resolution climate surfaces for the conterminous United States: the 1- km-resolution Daymet database of means for 1980–1997 (http://www.daymet.org/; Thornton et al., 1997) and the 2.5 arc min (∼5 km) PRISM climate database for 1970–2000 (http://www.ocs.orst.edu/; Daly et al., 2002)....
[...]
...Thornton et al. (1997) used a truncated Gaussian weighting filter in combination with spatially and temporally explicit empirically determined relationships of temperature and precipitation to elevation....
[...]
...…for the United * Correspondence to: Robert J. Hijmans, Museum of Vertebrate Zoology, University of California, 3101 Valley Life Sciences Building, Berkeley, CA, USA; e-mail: rhijmans@berkeley.edu Copyright 2005 Royal Meteorological Society States (http://www.daymet.org/; Thornton et al., 1997)....
[...]
...We then used SPLINA to build continuous climate surfaces for the training data and interrogated these surfaces for the locations of the test data....
[...]
...Weather station data were assembled from a large number of sources: (1) The Global Historical Climate Network Dataset (GHCN) version 2....
[...]
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).
Abstract: The demand for spatial climate data in digital form has risen dramatically in recent years. In response to this need, a variety of statistical techniques have been used to facilitate the pro- duction of GIS-compatible climate maps. However, observational data are often too sparse and unrepresentative to directly support the creation of high-quality climate maps and data sets that truly represent the current state of knowledge. An effective approach is to use the wealth of expert knowl- edge on the spatial patterns of climate and their relationships with geographic features, termed 'geospatial climatology', to help enhance, control, and parameterize a statistical technique. Described here is a dynamic knowledge-based framework that allows for the effective accumulation, application, and refinement of climatic knowledge, as expressed in a statistical regression model known as PRISM (parameter-elevation regressions on independent slopes model). The ultimate goal is to develop an expert system capable of reproducing the process a knowledgeable climatologist would use to create high-quality climate maps, with the added benefits of consistency and repeata- bility. However, knowledge must first be accumulated and evaluated through an ongoing process of model application; development of knowledge prototypes, parameters and parameter settings; test- ing; evaluation; and modification. This paper describes the current state of a knowledge-based framework for climate mapping and presents specific algorithms from PRISM to demonstrate how this framework is applied and refined to accommodate difficult climate mapping situations. A weighted climate-elevation regression function acknowledges the dominant influence of elevation on climate. Climate stations are assigned weights that account for other climatically important factors besides elevation. Aspect and topographic exposure, which affect climate at a variety of scales, from hill slope to windward and leeward sides of mountain ranges, are simulated by dividing the terrain into topographic facets. A coastal proximity measure is used to account for sharp climatic gradients near coastlines. A 2-layer model structure divides the atmosphere into a lower boundary layer and an upper free atmosphere layer, allowing the simulation of temperature inversions, as well as mid-slope precipitation maxima. The effectiveness of various terrain configurations at producing orographic precipitation enhancement is also estimated. Climate mapping examples are presented.
1,074 citations
"Very high resolution interpolated c..." refers background or methods in this paper
...GHCN has data for precipitation (20 590 stations), mean temperature (7280 stations), and minimum and maximum temperature (4966 stations)....
[...]
...We then used SPLINA to build continuous climate surfaces for the training data and interrogated these surfaces for the locations of the test data....
[...]
...…sets of high-resolution climate surfaces for the conterminous United States: the 1- km-resolution Daymet database of means for 1980–1997 (http://www.daymet.org/; Thornton et al., 1997) and the 2.5 arc min (∼5 km) PRISM climate database for 1970–2000 (http://www.ocs.orst.edu/; Daly et al., 2002)....
[...]
...Daly et al. (2002) used the PRISM method, which allows for incorporation of expert knowledge about the climate and can be particularly useful when data points are sparse....
[...]