Detection of Key Atmospheric Features in Reanalysis Data
Phillip Arkin1, Randy Dole2, Siegfried Schubert3, James Carton4, Eugenia Kalnay4, Randy
Koster3
1

Earth System Science Interdisciplinary Center, University of Maryland, USA
2
Earth System Research Lab, NOAA, USA3
Global Modeling and Assimilation Office, GSFC/NASA, USA
4
Department of Atmospheric and Oceanic Science, University of Maryland, USA
Correspondence: parkin@essic.umd.edu
INTRODUCTION
Beginning in the late 1980s, several reanalysis projects were initiated to develop long time records ofanalyses better suited for climate purposes (Table 1). The products of these first reanalyses have proven to beamong the most valuable and widely used in the history of climate science, as indicated both by the number ofscholarly publications that rely upon them and by their widespread use in current climate services. They haveproduced detailed atmospheric climate records that have enabled successful climate monitoring and research to beconducted. They have provided a vitally needed test bed for improving prediction models on all time scales (seenext section), especially for seasonal-to-interannual forecasts, as well as greatly improved basic observations anddata sets prepared for their production. Reanalysis, when extended to the present as an ongoing climate analysis,
provides decision-makers with information about current climate events in relation to past events, and contributes
directly to climate change assessments.
The US Climate Change Science Program has undertaken a comprehensive set of Synthesis and Analysis
Projects (SAP) to assess the current state of understanding of variability and change in the global climate system.
Since reanalysis data sets have become crucial to the description and understanding of atmospheric behavior, theirability to identify and characterize important features of atmospheric variability and change is crucial. SAP 1.3
is aimed at evaluating and assessing that capability and is the source for the material in this presentation. Since
the report is still undergoing review, specific details must be limited.
KEY FINDINGSThe report is organized into 4 chapters, the second of which is the source for this presentation. Chapter
takes the form of sections addressing a series of questions, with a set of specific conclusions, referred to as
findings, leading the material. Here we summarize those key findings as found in the draft report.
•Reanalysis plays a crucial integrating role within a global climate observing system by producing comprehensive
long-term, objective, and consistent records of climate system components, including the atmosphere, oceans, andland surface..

•Reanalysis data play a fundamental and unique role in studies that address the nature, causes and impacts ofglobal-scale and regional-scale climate phenomena.

•Reanalysis data sets are of particular value in studies of the physical mechanisms that produce high-impact
climate anomalies such as droughts and floods, as well as other key atmospheric features that affect the U.S.,
including climate variations associated with El Niño-Southern Oscillation and other major modes of climatevariability.

•Observed global and regional surface temperature trends are captured to first order in reanalysis data sets,
particularly since the late 1970s, although some regions continue to show major differences with observations

(e.g., Australia). Reanalysis precipitation trends are much less consistent with those calculated from observational
datasets, likely due principally to deficiencies in current global reanalysis models.
•The overall quality of reanalysis products varies with latitude, height, time period, spatial and temporal scale, andquantity or variable of interest.

•Current global reanalysis data are most reliable in Northern Hemisphere middle latitudes, in the middle to upper
troposphere, and on synoptic (weather) and larger spatial scales. They are least reliable near the surface, in thestratosphere, tropics, and polar regions.

•Current global reanalysis data are most reliable on daily to interannual time scales. They are least reliable in therepresentation of the diurnal cycle, and in the representation of decadal and longer time scales where they are
most impacted by deficiencies in the coverage and quality of observational data and changes in observing systems
over time.

•Current global reanalysis data are most reliable in quantities that are most strongly constrained by theobservations (e.g., temperature and winds), and least reliable for quantities that are highly model dependent, suchas evaporation, precipitation, and cloud-related quantities.

•

Substantial biases exist in various components of the atmospheric water cycle (e.g., precipitation, evaporation andclouds), that limit the value of current reanalysis data for assessing the veracity of these quantities in climate
models, as well as for practical applications. There are also significant biases in other surface and near-surface
quantities related to deficiencies in representing interactions across the land-atmosphere and ocean-atmosphereinterfaces.

•The comprehensive and multi-variate nature of reanalysis data provide value for understanding the causes ofsurface temperature and precipitation trends beyond what can be obtained from relatively incomplete
observational datasets alone, even in the face of the noted biases in reanalysis-based trends.

•Reanalysis data play a critical role in assessing the ability of climate models to simulate the statistics of climate –the means and variances (at various time scales) of basic variables such as the horizontal winds, temperature andpressure. In addition, the adjustments or analysis increments (i.e., the "corrections" imposed on model states by
the observations) produced during the course of a reanalysis provide a means to identify fundamental errors in thephysical processes and/or missing physics that create climate model biases•Reanalyses have had enormous benefits for climate research and prediction, as well as for a wide range of societal
applications. Nevertheless, significant future improvements are possible by developing new methods to address
observing system inhomogeneities, by developing estimates of the reanalysis uncertainties, by improving ourobservational database, and by developing integrated Earth system models and analysis systems that incorporate
key climate elements not included in atmospheric reanalyses to date.