An Evaluation of the Performance of the Twentieth Century Reanalysis Version 3

Abstract

The performance of a new historical reanalysis, the NOAA-CIRES-DOE 20th Century Reanalysis Version 3 (20CRv3), is evaluated via comparisons with other reanalyses and independent observations. This dataset provides global, 3-hourly estimates of the atmosphere from 1806 to 2015 by assimilating only surface pressure observations and prescribing sea surface temperature, sea ice concentration, and radiative forcings. Comparisons with independent observations, other reanalyses, and satellite products suggest that 20CRv3 can reliably produce atmospheric estimates on scales ranging from weather events to long-term climatic trends. Not only does 20CRv3 recreate a “best estimate” of the weather, including extreme events, it also provides an estimate of its confidence through the use of an ensemble. Surface pressure statistics suggest that these confidence estimates are reliable. Comparisons with independent upper-air observations in the Northern Hemisphere demonstrate that 20CRv3 has skill throughout the 20th century. Upper-air fields from 20CRv3 in the late 20th century and early 21st century correlate well with full-input reanalyses, and the correlation is predicted by the confidence fields from 20CRv3. The skill of analyzed 500hPa geopotential heights from 20CRv3 for 1979-2015 is comparable to that of modern operational 3- to 4-day forecasts. Finally, 20CRv3 performs well on climate timescales. Long time series and multidecadal averages of mass, circulation, and precipitation fields agree well with modern reanalyses and station- and satellite-based products. 20CRv3 is also able to capture trends in tropospheric layer temperatures that correlate well with independent products in the 20th century, placing recent trends in a longer historical context.

Detecting Climate Signals Using Explainable AI With Single‐Forcing Large Ensembles

“…These improvements lead to a reduction in biases of near‐surface temperature, sea surface temperature, and sea level pressure compared to older versions of 20CR, especially in the early to mid‐20th century (Compo et al., 2011; Giese et al., 2016). Further, 20CRv3 was found to be in close agreement with other independently derived reanalysis data sets, including the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐20C and CERA‐20C (Slivinski et al., 2019, 2020).…”

A historical climate dataset for southwestern Australia, 1830–1875

“…The incorporation of historical observations from the Australian region will also help improve data coverage in global datasets like the Twentieth Century Reanalysis that can be used to better resolve the dynamics of past weather and climate variability and extremes (Slivinski et al ., 2021). This will allow a detailed analysis of the circulation changes being experienced in the sub‐tropical regions of the Southern Hemisphere that are already showing sensitivity to anthropogenically driven climate change.…”

“…(2020) and use the ensemble mean of the 20th Century Reanalysis version 3 (20CRv3; Slivinski et al ., 2019). This version contains recently recovered surface pressure observations from the 19th and early 20th century from Australia (Ashcroft et al ., 2014a; 2014b), potentially improving its regional performance over this period (Slivinski et al ., 2019; 2021).…”

“…The main aim of this initiative is to improve reconstructions of global historical weather conditions primarily through the development of the Twentieth Century Reanalysis product (Compo et al ., 2011; Slivinski et al ., 2019). This product reconstructs synoptic conditions using sub‐daily surface pressure observations as input and observed monthly sea surface temperatures and sea ice conditions as boundary conditions (Compo et al ., 2011; Slivinski et al ., 2019), allowing the dynamical conditions of past extremes to be examined in greater detail (Slivinski et al ., 2021).…”

Comparisons Between CMIP5 and CMIP6 Models: Simulations of Climate Indices Influencing Food Security, Infrastructure Resilience, and Human Health in Canada

“…This study considers a historical period of 1961–1990 as well as short‐term (2021–2050) and long‐term (2071–2100) future horizons. Moreover, the daily P, Tmax, and Tmin from two different reanalysis datasets, namely NCEP (Kalnay et al., 1996) and NOAA (Compo et al., 2011; Slivinski et al., 2019, 2020) are considered here to represent the historical climate conditions (Sillman et al., 2013) over Canada and benchmark the performance of CMIPs during this period. Due to the mismatch among the spatial scale of climate models and reanalyzes, the Nearest Neighbor algorithm (Schulzweida et al., 2006) from the Climate Data Operator (https://code.zmaw.de/projects/cdo) is considered to regrid the model outputs to a common resolution of 1.5°.…”