An Overview of the Global Historical Climatology Network-Daily Database
01 Jul 2012-Journal of Atmospheric and Oceanic Technology (American Meteorological Society)-Vol. 29, Iss: 7, pp 897-910
Abstract: A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic...
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TL;DR: New global maps of the Köppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day and for projected future conditions under climate change are presented, providing valuable indications of the reliability of the classifications.
Abstract: We present new global maps of the Koppen-Geiger climate classification at an unprecedented 1-km resolution for the present-day (1980–2016) and for projected future conditions (2071–2100) under climate change. The present-day map is derived from an ensemble of four high-resolution, topographically-corrected climatic maps. The future map is derived from an ensemble of 32 climate model projections (scenario RCP8.5), by superimposing the projected climate change anomaly on the baseline high-resolution climatic maps. For both time periods we calculate confidence levels from the ensemble spread, providing valuable indications of the reliability of the classifications. The new maps exhibit a higher classification accuracy and substantially more detail than previous maps, particularly in regions with sharp spatial or elevation gradients. We anticipate the new maps will be useful for numerous applications, including species and vegetation distribution modeling. The new maps including the associated confidence maps are freely available via www.gloh2o.org/koppen
. Machine-accessible metadata file describing the reported data (ISA-Tab format)
2,434 citations
TL;DR: In this article, the Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst has calculated a precipitation climatology for the global land areas for the target period 1951-2000 by objective analysis of climatological normals of about 67,200 rain gauge stations from its data base.
Abstract: In 1989, the need for reliable gridded land surface precipitation data sets, in view of the large uncertainties in the assessment of the global energy and water cycle, has led to the establishment of the Global Precipitation Climatology Centre (GPCC) at Deutscher Wetterdienst on invitation of the WMO. The GPCC has calculated a precipitation climatology for the global land areas for the target period 1951–2000 by objective analysis of climatological normals of about 67,200 rain gauge stations from its data base. GPCC's new precipitation climatology is compared to several other station-based precipitation climatologies as well as to precipitation climatologies derived from the GPCP V2.2 data set and from ECMWF's model reanalyses ERA-40 and ERA-Interim. Finally, how GPCC's best estimate for terrestrial mean precipitation derived from the precipitation climatology of 786 mm per year (equivalent to a water transport of 117,000 km3) is fitting into the global water cycle context is discussed.
1,107 citations
Cites methods from "An Overview of the Global Historica..."
...2, supplemented by monthly totals calculated from the GHCN daily data set (Menne et al. 2012), which is an ongoing activity at GPCC....
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...With the beginning of 2012, the GPCC started the acquisition and processing of daily precipitation data and is working on the integration of the GHCN daily data set (Menne et al. 2012) and several national collections of daily data provided by the NMHSs to the GPCC into its data base system....
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...The station number for GHCN is consisting of 20,590 stations from GHCNV.2, supplemented by monthly totals calculated from the GHCN daily data set (Menne et al. 2012), which is an ongoing activity at GPCC....
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TL;DR: TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets, as well as annual runoff from streamflow gauges.
Abstract: We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.
1,079 citations
University of New South Wales1, National Oceanic and Atmospheric Administration2, Met Office3, University of East Anglia4, University of Cape Town5, Royal Netherlands Meteorological Institute6, South African Weather Service7, National Institute for Space Research8, University of the Republic9, University of Buenos Aires10, Stanford University11, India Meteorological Department12, Bureau of Meteorology13, Environment Canada14, Newcastle University15
TL;DR: In this paper, the authors presented the collation and analysis of the gridded land-based dataset of indices of temperature and precipitation extremes: HadEX2, which was calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices.
Abstract: [1] In this study, we present the collation and analysis of the gridded land-based dataset of indices of temperature and precipitation extremes: HadEX2. Indices were calculated based on station data using a consistent approach recommended by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices, resulting in the production of 17 temperature and 12 precipitation indices derived from daily maximum and minimum temperature and precipitation observations. High-quality in situ observations from over 7000 temperature and 11,000 precipitation meteorological stations across the globe were obtained to calculate the indices over the period of record available for each station. Monthly and annual indices were then interpolated onto a 3.75° × 2.5° longitude-latitude grid over the period 1901–2010. Linear trends in the gridded fields were computed and tested for statistical significance. Overall there was very good agreement with the previous HadEX dataset during the overlapping data period. Results showed widespread significant changes in temperature extremes consistent with warming, especially for those indices derived from daily minimum temperature over the whole 110 years of record but with stronger trends in more recent decades. Seasonal results showed significant warming in all seasons but more so in the colder months. Precipitation indices also showed widespread and significant trends, but the changes were much more spatially heterogeneous compared with temperature changes. However, results indicated more areas with significant increasing trends in extreme precipitation amounts, intensity, and frequency than areas with decreasing trends.
1,055 citations
Cites methods from "An Overview of the Global Historica..."
...The Global Historical Climatology Network-Daily (GHCN-Daily) [Menne et al., 2012]....
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TL;DR: In this paper, the authors investigated the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009.
Abstract: This study investigates the presence of trends in annual maximum daily precipitation time series obtained from a global dataset of 8326 high-quality land-based observing stations with more than 30 years of record over the period from 1900 to 2009. Two complementary statistical techniques were adopted to evaluate the possible nonstationary behavior of these precipitation data. The first was a Mann‐Kendall nonparametric trend test, and it was used to evaluate the existence of monotonic trends. The second was a nonstationary generalized extreme value analysis, and it was used to determine the strength of association between the precipitation extremes and globally averaged near-surface temperature. The outcomes are that statistically significant increasing trends can be detected at the global scale, with close to two-thirds of stations showing increases. Furthermore, there is a statistically significant association with globally averaged near-surface temperature,withthemedianintensityofextremeprecipitationchanginginproportionwithchangesinglobal mean temperature at a rate of between 5.9% and 7.7%K 21 , depending on the method of analysis. This ratio was robust irrespective of record length or time period considered and was not strongly biased by the uneven global coverage of precipitation data. Finally, there is a distinct meridional variation, with the greatest sensitivity occurring in the tropics and higher latitudes and the minima around 138S and 118N. The greatest uncertainty was near the equator because of the limited number of sufficiently long precipitation records, and there remains an urgent need to improve data collection in this region to better constrain future changes in tropical precipitation.
825 citations
Cites background from "An Overview of the Global Historica..."
...While there are other datasets that contain more in situ daily precipitation observations, for example, Global Historical Climatology Network (GHCN)-Daily (Durre et al. 2010; Menne et al. 2012), these data are not quality controlled [although quality assurance flags are available for each data point (Durre et al....
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...…that contain more in situ daily precipitation observations, for example, Global Historical Climatology Network (GHCN)-Daily (Durre et al. 2010; Menne et al. 2012), these data are not quality controlled [although quality assurance flags are available for each data point (Durre et al. 2010)]…...
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References
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6,121 citations
TL;DR: In this paper, the goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed.
Abstract: Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness-of-fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation-based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation-based measures should not be used to assess the goodness-of-fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.
3,891 citations
Monash University, Clayton campus1, Bureau of Meteorology2, Met Office3, Meteorological Service of Canada4, National Oceanic and Atmospheric Administration5, Royal Netherlands Meteorological Institute6, University of East Anglia7, Indian Institute of Tropical Meteorology8, National Institute of Water and Atmospheric Research9, University of Reading10, University of the West Indies11, University of Oxford12, China Meteorological Administration13, Facultad de Ciencias Exactas y Naturales14, National Autonomous University of Mexico15
TL;DR: A suite of climate change indices derived from daily temperature and precipitation data, with a primary focus on extreme events, were computed and analyzed as discussed by the authors, and the results showed widespread significant changes in temperature extremes associated with warming.
Abstract: A suite of climate change indices derived from daily temperature and precipitation data, with a primary focus on extreme events, were computed and analyzed. By setting an exact formula for each index and using specially designed software, analyses done in different countries have been combined seamlessly. This has enabled the presentation of the most up-to-date and comprehensive global picture of trends in extreme temperature and precipitation indices using results from a number of workshops held in data-sparse regions and high-quality station data supplied by numerous scientists world wide. Seasonal and annual indices for the period 1951-2003 were gridded. Trends in the gridded fields were computed and tested for statistical significance. Results showed widespread significant changes in temperature extremes associated with warming, especially for those indices derived from daily minimum temperature. Over 70% of the global land area sampled showed a significant decrease in the annual occurrence of cold nights and a significant increase in the annual occurrence of warm nights. Some regions experienced a more than doubling of these indices. This implies a positive shift in the distribution of daily minimum temperature throughout the globe. Daily maximum temperature indices showed similar changes but with smaller magnitudes. Precipitation changes showed a widespread and significant increase, but the changes are much less spatially coherent compared with temperature change. Probability distributions of indices derived from approximately 200 temperature and 600 precipitation stations, with near-complete data for 1901-2003 and covering a very large region of the Northern Hemisphere midlatitudes (and parts of Australia for precipitation) were analyzed for the periods 1901-1950, 1951-1978 and 1979-2003. Results indicate a significant warming throughout the 20th century. Differences in temperature indices distributions are particularly pronounced between the most recent two periods and for those indices related to minimum temperature. An analysis of those indices for which seasonal time series are available shows that these changes occur for all seasons although they are generally least pronounced for September to November. Precipitation indices show a tendency toward wetter conditions throughout the 20th century.
3,722 citations
"An Overview of the Global Historica..." refers background in this paper
...Unfortunately, daily data are comparatively less accessible than monthly values, in part because of the reluctance in many countries to release daily climate summaries for widespread public use (Alexander et al. 2006)....
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Book Chapter•
01 Jan 20072,595 citations
Additional excerpts
...analysis and model comparison studies (Trenberth et al. 2007)....
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TL;DR: It is shown that human-induced increases in greenhouse gases have contributed to the observed intensification of heavy precipitation events found over approximately two-thirds of data-covered parts of Northern Hemisphere land areas.
Abstract: A significant effect of anthropogenic activities has already been detected in observed trends in temperature and mean precipitation. But to date, no study has formally identified such a human fingerprint on extreme precipitation — an increase in which is one of the central theoretical expectations for a warming climate. Seung-Ki Min and colleagues compare observations and simulations of rainfall between 1951 and 1999 in North America, Europe and northern Asia. They find a statistically significant effect of increased greenhouse gases on observed increases in extreme precipitation events over much of the Northern Hemisphere land area. A significant effect of anthropogenic activities has already been detected in observed trends in temperature and mean precipitation. But so far, no study has formally identified such a human fingerprint on extreme precipitation — an increase in which is one of the central theoretical expectations for a warming climate. This study compares observations and simulations and detects a statistically significant effect of increased greenhouse gases on observed increases in extreme precipitation events over much of the Northern Hemisphere land area. Extremes of weather and climate can have devastating effects on human society and the environment1,2. Understanding past changes in the characteristics of such events, including recent increases in the intensity of heavy precipitation events over a large part of the Northern Hemisphere land area3,4,5, is critical for reliable projections of future changes. Given that atmospheric water-holding capacity is expected to increase roughly exponentially with temperature—and that atmospheric water content is increasing in accord with this theoretical expectation6,7,8,9,10,11—it has been suggested that human-influenced global warming may be partly responsible for increases in heavy precipitation3,5,7. Because of the limited availability of daily observations, however, most previous studies have examined only the potential detectability of changes in extreme precipitation through model–model comparisons12,13,14,15. Here we show that human-induced increases in greenhouse gases have contributed to the observed intensification of heavy precipitation events found over approximately two-thirds of data-covered parts of Northern Hemisphere land areas. These results are based on a comparison of observed and multi-model simulated changes in extreme precipitation over the latter half of the twentieth century analysed with an optimal fingerprinting technique. Changes in extreme precipitation projected by models, and thus the impacts of future changes in extreme precipitation, may be underestimated because models seem to underestimate the observed increase in heavy precipitation with warming16.
1,773 citations
"An Overview of the Global Historica..." refers background in this paper
...2004), changes in the frequency of heavy precipitation (Min et al. 2011), and changes in heat wave frequency and duration (Della Marta et al....
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...For example, the analysis of changes in the length of the growing season (Kunkel et al. 2004), changes in the frequency of heavy precipitation (Min et al. 2011), and changes in heat wave frequency and duration (Della Marta et al. 2007) all require data at least at the daily resolution....
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