Dataset Information

A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations.

ABSTRACT: Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressure observations, temperature indices derived from historical documents and climate sensitive tree-ring measurements were assimilated into an atmospheric general circulation model ensemble using a Kalman filtering technique. This data set combines the advantage of traditional reconstruction methods of being as close as possible to observations with the advantage of climate models of being physically consistent and having 3-dimensional information about the state of the atmosphere for various variables and at all points in time. In contrast to most statistical reconstructions, centennial variability stems from the climate model and its forcings, no stationarity assumptions are made and error estimates are provided.

SUBMITTER: Franke J

PROVIDER: S-EPMC5460593 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations.

Franke Jörg J Brönnimann Stefan S Bhend Jonas J Brugnara Yuri Y

Scientific data 20170606

Climatic variations at decadal scales such as phases of accelerated warming or weak monsoons have profound effects on society and economy. Studying these variations requires insights from the past. However, most current reconstructions provide either time series or fields of regional surface climate, which limit our understanding of the underlying dynamics. Here, we present the first monthly paleo-reanalysis covering the period 1600 to 2005. Over land, instrumental temperature and surface pressu ...[more]

PMID: 28585926

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Project description:Abstract Data assimilation techniques are becoming increasingly popular for climate reconstruction. They benefit from estimating past climate states from both observation information and from model simulations. The first monthly global paleo‐reanalysis (EKF400) was generated over the 1600 and 2005 time period, and it provides estimates of several atmospheric fields. Here we present a new, considerably improved version of EKF400 (EKF400v2). EKF400v2 uses atmospheric‐only general circulation model simulations with a greatly extended observational network of early instrumental temperature and pressure data, documentary evidences and tree‐ring width and density proxy records. Furthermore, new observation types such as monthly precipitation amounts, number of wet days and coral proxy records were also included in the assimilation. In the version 2 system, the assimilation process has undergone methodological improvements such as the background‐error covariance matrix is estimated with a blending technique of a time‐dependent and a climatological covariance matrices. In general, the applied modifications resulted in enhanced reconstruction skill compared to version 1, especially in precipitation, sea‐level pressure and other variables beside the mostly assimilated temperature data, which already had high quality in the previous version. Additionally, two case studies are presented to demonstrate the applicability of EKF400v2 to analyse past climate variations and extreme events, as well as to investigate large‐scale climate dynamics. This paper presents the EKF400v2 global monthly paleo‐reanalysis, generated with a paleoclimate data assimilation method, covering the last 400 years. The paleo‐reanalysis provides several climate fields such as temperature, precipitation and sea‐level pressure. We compare EKF400v2 to a previous version, describe the extended observational network, the model simulations and highlight the applied modifications to the assimilation system. The paleo‐reanalysis is evaluated in the 20th century, and two case studies demonstrate the usage of the dataset to investigate past climate variability.

Project description:Along with global climate change, the occurrence of extreme droughts in recent years has had a serious impact on the Amazon region. Current studies on the driving factors of the 2005 and 2010 Amazon droughts has focused on the influence of precipitation, whereas the impacts of temperature and radiation have received less attention. This study aims to explore the climate-driven factors of Amazonian vegetation decline during the extreme droughts using vegetation index, precipitation, temperature and radiation datasets. First, time-lag effects of Amazonian vegetation responses to precipitation, radiation and temperature were analyzed. Then, a multiple linear regression model was established to estimate the contributions of climatic factors to vegetation greenness, from which the dominant climate-driving factors were determined. Finally, the climate-driven factors of Amazonian vegetation greenness decline during the 2005 and 2010 extreme droughts were explored. The results showed that (i) in the Amazon vegetation greenness responded to precipitation, radiation and temperature, with apparent time lags for most averaging interval periods associated with vegetation index responses of 0-4, 0-9 and 0-6 months, respectively; (ii) on average, the three climatic factors without time lags explained 27.28±21.73% (mean±1 SD) of vegetation index variation in the Amazon basin, and this value increased by 12.22% and reached 39.50±27.85% when time lags were considered; (iii) vegetation greenness in this region in non-drought years was primarily affected by precipitation and shortwave radiation, and these two factors altogether accounted for 93.47% of the total explanation; and (iv) in the common epicenter of the two droughts, pixels with a significant variation in precipitation, radiation and temperature accounted for 36.68%, 40.07% and 10.40%, respectively, of all pixels showing a significant decrease in vegetation index in 2005, and 15.69%, 2.01% and 45.25% in 2010, respectively. Overall, vegetation greenness declines during the 2005 and 2010 extreme droughts were adversely influenced by precipitation, radiation and temperature; this study provides evidence of the influence of multiple climatic factors on vegetation during the 2005 and 2010 Amazon droughts.

Dataset Information

A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations.

Publications

A monthly global paleo-reanalysis of the atmosphere from 1600 to 2005 for studying past climatic variations.

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