4.7 Article

Recent and Projected Annual Cycles of Temperature and Precipitation in the Northeast United States from CMIP5

Journal

JOURNAL OF CLIMATE
Volume 29, Issue 1, Pages 347-365

Publisher

AMER METEOROLOGICAL SOC
DOI: 10.1175/JCLI-D-14-00781.1

Keywords

Geographic location; entity; North America; Physical Meteorology and Climatology; Climate change; Models and modeling; Coupled models; Variability; Seasonal cycle

Funding

  1. NSF CAREER Award [1056216]

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A case study is presented using the northeast United States to evaluate information contained in the monthly mean annual cycle that has yet to be exploited. This research documents the performance and projections for the northeast United States from a suite of 16 climate models in the archive of phase 5 of the Coupled Model Intercomparison Project (CMIP5) from the World Climate Research Programme (WCRP). Analysis is performed for the late twentieth-century monthly mean annual cycle and changes in the late twenty-first century. A weak seasonality in temperature and a strong seasonality in precipitation changes are found. The seasonality of changes is distinct from the mean annual cycles, such that temperature increases are largest in midwinter (December-February) and late summer [July-September (JAS)]. Precipitation increases peak in late winter-early spring (February-April), associated with increased moisture convergence and a more active storm track, and exhibit greatest model disagreement in late summer (JAS) when the models suggest weak divergence and a westward extension of the Atlantic subtropical anticyclone. The late summer-early fall maximum in temperature and late winter-early spring maximum in precipitation changes have not been seen previously in annual or seasonal mean analyses. Yet there is model agreement in these results, indicating that there is important information in the annual cycle for understanding the changes in the physical climate system and for evaluating impacts and adaptation strategies. It is argued that improved understanding of seasonal transitions has potential to increase confidence in projections, and to provide additional information of use to the impacts and decision-maker communities.

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