skip to main content


This content will become publicly available on December 1, 2024

Title: The global historical climate database HCLIM
Abstract

There is a growing need for past weather and climate data to support science and decision-making. This paper describes the compilation and construction of a global multivariable (air temperature, pressure, precipitation sum, number of precipitation days) monthly instrumental climate database that encompasses a substantial body of the known early instrumental time series. The dataset contains series compiled from existing databases that start before 1890 (though continuing to the present) as well as a large amount of newly rescued data. All series underwent a quality control procedure and subdaily series were processed to monthly mean values. An inventory was compiled, and the collection was deduplicated based on coordinates and mutual correlations. The data are provided in a common format accompanied by the inventory. The collection totals 12452 meteorological records in 118 countries. The data can be used for climate reconstructions and analyses. It is the most comprehensive global monthly climate dataset for the preindustrial period so far.

 
more » « less
Award ID(s):
1854511
NSF-PAR ID:
10477363
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more » ; ; ; ; ; ; ; ; « less
Publisher / Repository:
NSF-PAR
Date Published:
Journal Name:
Scientific Data
Volume:
10
Issue:
1
ISSN:
2052-4463
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    Documentary climate data describe evidence of past climate arising from predominantly written historical documents such as diaries, chronicles, newspapers, or logbooks. Over the past decades, historians and climatologists have generated numerous document-based time series of local and regional climates. However, a global dataset of documentary climate time series has never been compiled, and documentary data are rarely used in large-scale climate reconstructions. Here, we present the first global multi-variable collection of documentary climate records. The dataset DOCU-CLIM comprises 621 time series (both published and hitherto unpublished) providing information on historical variations in temperature, precipitation, and wind regime. The series are evaluated by formulating proxy forward models (i.e., predicting the documentary observations from climate fields) in an overlapping period. Results show strong correlations, particularly for the temperature-sensitive series. Correlations are somewhat lower for precipitation-sensitive series. Overall, we ascribe considerable potential to documentary records as climate data, especially in regions and seasons not well represented by early instrumental data and palaeoclimate proxies.

     
    more » « less
  2. Abstract

    The interior of Dronning Maud Land (DML) in East Antarctica is one of the most data-sparse regions of Antarctica for studying climate change. A monthly mean near-surface temperature dataset for the last 30 years has been compiled from the historical records from automatic weather stations (AWSs) at three sites in the region (Mizuho, Relay Station, and Dome Fuji). Multiple AWSs have been installed along the route to Dome Fuji since the 1990s, and observations have continued to the present day. The use of passive-ventilated radiation shields for the temperature sensors at the AWSs may have caused a warm bias in the temperature measurements, however, due to insufficient ventilation in the summer, when solar radiation is high and winds are low. In this study, these warm biases are quantified by comparison with temperature measurements with an aspirated shield and subsequently removed using a regression model. Systematic error resulting from changes in the sensor height due to accumulating snow was insignificant in our study area. Several other systematic errors occurring in the early days of the AWS systems were identified and corrected. After the corrections, multiple AWS records were integrated to create a time series for each station. The percentage of missing data over the three decades was 21% for Relay Station and 28% for Dome Fuji. The missing rate at Mizuho was 49%, more than double that at Relay Station. These new records allow for the study of temperature variability and change in DML, where climate change has so far been largely unexplored.

    Significance Statement

    Antarctic climate change has been studied using temperature data at staffed stations. The staffed stations, however, are mainly located on the Antarctic Peninsula and in the coastal regions. Climate change is largely unknown in the Antarctic plateau, particularly in the western sector of the East Antarctic Plateau in areas such as the interior of Dronning Maud Land (DML). To fill the data gap, this study presents a new dataset of monthly mean near-surface climate data using historical observations from three automatic weather stations (AWSs). This dataset allows us to study temperature variability and change over a data-sparse region where climate change has been largely unexplored.

     
    more » « less
  3. Abstract. Recent observations of near-surface soil temperatures over the circumpolarArctic show accelerated warming of permafrost-affected soils. Theavailability of a comprehensive near-surface permafrost and active layerdataset is critical to better understanding climate impacts and toconstraining permafrost thermal conditions and its spatial distribution inland system models. We compiled a soil temperature dataset from 72 monitoringstations in Alaska using data collected by the U.S. Geological Survey, theNational Park Service, and the University of Alaska Fairbanks permafrostmonitoring networks. The array of monitoring stations spans a large range oflatitudes from 60.9 to 71.3N and elevations from near sea level to∼1300m, comprising tundra and boreal forest regions. This datasetconsists of monthly ground temperatures at depths up to 1m,volumetric soil water content, snow depth, and air temperature during1997–2016. These data have been quality controlled in collection andprocessing. Meanwhile, we implemented data harmonization evaluation for theprocessed dataset. The final product (PF-AK, v0.1) is available at the ArcticData Center (https://doi.org/10.18739/A2KG55).

     
    more » « less
  4. Abstract

    Mean daily to monthly precipitation averages peak in late July over eastern Colorado and some of the most damaging Front Range flash floods have occurred because of extreme 1-day rainfall events during this period. Tree-ring chronologies of adjusted latewood width in ponderosa pine from eastern Colorado are highly correlated with the highest 1-day rainfall totals occurring during this 2-week precipitation maximum in late July. A regional average of four adjusted latewood chronologies from eastern Colorado was used to reconstruct the single wettest day observed during the last two weeks of July. The regional chronology was calibrated with the CPC 0.25° × 0.25° Daily U.S. Unified Gauge-Based Analysis of Precipitation dataset and explains 65% of the variance in the highest 1-day late July precipitation totals in the instrumental data from 1948 to 1997. The reconstruction and instrumental data extend fully from 1779 to 2019 and indicate that the frequency of 1-day rainfall extremes in late July has increased since the late eighteenth century. The largest instrumental and reconstructed 1-day precipitation extremes are most commonly associated with the intrusion of a major frontal system into a deep layer of atmospheric moisture across eastern Colorado. These general synoptic conditions have been previously linked to extreme localized rainfall totals and widespread thunderstorm activity over Colorado during the summer season. Chronologies of adjusted latewood width in semiarid eastern Colorado constitute a proxy of weather time-scale rainfall events useful for investigations of long-term variability and for framing natural and potential anthropogenic forcing of precipitation extremes during this 2-week precipitation maximum in a long historical perspective.

     
    more » « less
  5. Abstract

    This study presents a novel, high-resolution, dynamically downscaled dataset that will help inform regional and local stakeholders regarding potential impacts of climate change at the scales necessary to examine extreme mesoscale conditions. WRF-ARW version 4.1.2 was used in a convection-permitting configuration (horizontal grid spacing of 3.75 km; 51 vertical levels; data output interval of 15-min) as a regional climate model for a domain covering the contiguous US Initial and lateral boundary forcing for the regional climate model originates from a global climate model simulation by NCAR (Community Earth System Model) that participated in phase 5 of the Coupled Model Inter comparison Project. Herein, we use a version of these data that are regridded and bias corrected. Two 15-year downscaled simulation epochs were examined comprising of historical (HIST; 1990–2005) and potential future (FUTR; 2085–2100) climate using Representative Concentration Pathway (RCP) 8.5. HIST verification against independent observational data revealed that annual/seasonal/monthly temperature and precipitation (and their extremes) are replicated admirably in the downscaled HIST epoch, with the largest biases in temperature noted with daily maximum temperatures (too cold) and the largest biases in precipitation (too dry) across the southeast US during the boreal warm season. The simulations herein are improved compared to previous work, which is significant considering the differences in previous modeling approaches. Future projections of temperature under the RCP 8.5 scenario are consistent with previous works using various methods. Future precipitation projections suggest statistically significant decreases of precipitation across large segments of the southern Great Plains and Intermountain West, whereas significant increases were noted in the Tennessee/Ohio Valleys and across portions of the Pacific Northwest. Overall, these simulations serve as an additional datapoint/method to detect potential future changes in extreme meso-γ weather phenomena.

     
    more » « less