Assimilating monthly precipitation data in a paleoclimate data assimilation framework

Abstract

Abstract. Data assimilation approaches such as the ensemble Kalman filter method have become an important technique for paleoclimatological reconstructions and reanalysis. Different sources of information from proxy records and documentary data to instrumental measurements were assimilated in previous studies to reconstruct past climate fields. However, precipitation reconstructions are often based on indirect sources (e.g., proxy records). Assimilating precipitation measurements is a challenging task because they have high uncertainties, often represent only a small region and generally do not follow Gaussian distribution. In this paper, a set of experiments are conducted to test the possibility of using information about precipitation in climate reconstruction with monthly resolution by assimilating monthly instrumental precipitation amounts or the number of wet days per month, solely or in addition to other climate variables such as temperature and sea level pressure, into an ensemble of climate model simulations. The skill of all variables (temperature, precipitation, sea-level pressure) improved over the pure model simulations when only monthly precipitation amounts were assimilated. Assimilating the number of wet days resulted in similar or better skill compared to assimilating precipitation amount. The experiments with different types of instrumental observations being assimilated indicate that precipitation data can be useful, particularly if no other variable is available from a given region. Overall the experiments show promising results because with the assimilation of precipitation information a new data source can be exploited for climate reconstructions. Especially the wet day records can become an important data source in future climate reconstructions because many existing records date several centuries back in time and are not limited by the availability of meteorological instruments.

An updated global atmospheric paleo‐reanalysis covering the last 400 years

“…The prescribed observational error of instrumental temperature and sea‐level pressure measurements are √0.9 K and √10 hPa, respectively. The error in instrumental precipitation measurement is estimated as 30% of the measured data or at least 10 mm, and the estimated error in the number of wet days is 2 days (Valler et al, 2020). The assigned error of documentary data is 0.5 standard deviations.…”

“…The observational network of previously assimilated data types is notably extended. In addition, in version 2 of EKF400, new data sources such as corals and precipitation information (amounts and number of wet days) were assimilated (Valler et al, 2020). Another line of improvements focuses on methodological developments.…”

Using a process-based dendroclimatic proxy system model in a data assimilation framework: a test case in the Southern Hemisphere over the past centuries

“…In paleoclimatology, the objective of data assimilation is to optimally combine the information from the physics of the climate system as included in climate models and indirect observations of climate from proxies (Kalnay, 2003;Goosse et al, 2010;Widmann et al, 2010;Steiger et al, 2014;Franke et al, 2017;Hakim et al, 2016). More specifically, the DA procedure is based on Bayes' theorem (van Leeuwen, 2009): starting from the prior estimate of the state of the climate system provided by the model, DA produces a reconstruction of the climate system that is as accurate as possible (i.e., the posterior) given the information provided by the climate observations. The updated estimate of the climate system is often called a reanalysis.…”

Global variations in regional degradation rates since the Last Glacial Maximum mapped through time and space

“…However, while precipitation conventionally has been assumed as a primary agent in sediment transport, precipitation records are problematic to implement in generating climate metrics that change through time globally for the following reasons: (1) they are limited to the instrumental record; (2) they have large spatial heterogeneity and (3) higher uncertainty than temperature records; and (4) mechanisms producing precipitation are not assimilated in climate models, as temperature has higher fidelity and is better representative of changes in Earth's energy budget. In fact, temperature is used as a benchmark in global climate modeling, as it relates to all other changes in the climate system, and so it is reflective of global climate changes (Legates, 2014; Zhu et al, 2019; Valler et al, 2020). This coincides with what is understood about the climate system, whose components, such as land, vegetation, ice, ocean, and atmosphere, interact and are changed by radiative forcing as a major driver (Ruddiman, 2014).…”