How distinct are the two flavors of El Niño in retrospective forecasts of Climate Forecast System version 2 (CFSv2)?

2016 ◽  
Vol 48 (11-12) ◽  
pp. 3829-3854 ◽  
Author(s):  
Prasanth A. Pillai ◽  
Suryachandra A. Rao ◽  
Gibies George ◽  
D. Nagarjuna Rao ◽  
S. Mahapatra ◽  
...  
2011 ◽  
Vol 24 (17) ◽  
pp. 4676-4694 ◽  
Author(s):  
Scott J. Weaver ◽  
Wanqiu Wang ◽  
Mingyue Chen ◽  
Arun Kumar

The Madden–Julian oscillation (MJO) is arguably the most important intraseasonal mode of climate variability, given its significant modulation of global climate variations and attendant societal impacts. Advancing the current understanding and simulation of the MJO using state-of-the-art climate data and modeling systems is thus a necessary goal for improving MJO prediction capability. MJO variability is assessed in NOAA/NCEP reanalyses and two versions of the Climate Forecast System (CFS), CFS version 1 (CFSv1) and its update version 2 (CFSv2). The analysis leans on a variety of diagnostic procedures and includes MJO sensitivity to varying El Niño–Southern Oscillation (ENSO) phases. It is found that significant improvements have been realized in the representation of MJO variations in the new NCEP Climate Forecast System reanalysis (CFSR) as evidenced by outgoing longwave radiation (OLR) power spectral analysis and more coherent propagation characteristics of precipitation and 850-hPa zonal winds over the Eastern Hemisphere in CFSR-only depictions. Conversely, while modest improvements are realized in the CFSv2 as compared to CFSv1, in general the simulation of the MJO continues to be a challenge. Both versions produce strong eastward propagating variance of convection and wind fields in the intraseasonal frequency band. However, the simulated MJO propagates slower than the observed with difficulties traversing the Maritime Continent into the western Pacific, as noted in many previous modeling studies. The CFS shows robust intraseasonal simulations over the west Pacific during El Niño years with diminished simulation capability over the Indian Ocean during La Niña years. This is likely a manifestation of the preference for La Niña MJO activity to occur over the Indian Ocean and the simulation challenges over that domain.


2015 ◽  
Vol 28 (3) ◽  
pp. 1166-1183 ◽  
Author(s):  
Lejiang Yu ◽  
Shiyuan Zhong ◽  
Xindi Bian ◽  
Warren E. Heilman

Abstract This study examines the spatial and temporal variability of wind speed at 80 m above ground (the average hub height of most modern wind turbines) in the contiguous United States using Climate Forecast System Reanalysis (CFSR) data from 1979 to 2011. The mean 80-m wind exhibits strong seasonality and large spatial variability, with higher (lower) wind speeds in the winter (summer), and higher (lower) speeds over much of the Midwest and U.S. Northeast (U.S. West and Southeast). Trends are also variable spatially, with more upward trends in areas of the Great Plains and Intermountain West of the United States and more downward trends elsewhere. The leading EOF mode, which accounts for 20% (summer) to 33% (winter) of the total variance and represents in-phase variations across the United States, responds mainly to the North Atlantic Oscillation (NAO) in summer and El Niño–Southern Oscillation (ENSO) in the other seasons. The dominant variation pattern can be explained by a southerly/southwesterly (westerly) anomaly over the U.S. East (U.S. West) as a result of the anomalous mean sea level pressure (MSLP) pattern. The second EOF mode, which explains about 15% of the total variance and shows a seesaw pattern, is mainly related to the springtime Arctic Oscillation (AO), the summertime recurrent circumglobal teleconnection (CGT), the autumn Pacific decadal oscillation (PDO), and the winter El Niño Modoki. The anomalous jet stream and MSLP patterns associated with these indices are responsible for the wind variation.


2011 ◽  
Vol 39 (1-2) ◽  
pp. 365-381 ◽  
Author(s):  
Boyin Huang ◽  
Yan Xue ◽  
Hui Wang ◽  
Wanqiu Wang ◽  
Arun Kumar

2014 ◽  
Vol 15 (3) ◽  
pp. 1166-1188 ◽  
Author(s):  
Di Tian ◽  
Christopher J. Martinez ◽  
Wendy D. Graham

AbstractReference evapotranspiration (ETo) is an important hydroclimatic variable for water planning and management. This research explored the potential of using the Climate Forecast System, version 2 (CFSv2), for seasonal predictions of ETo over the states of Alabama, Georgia, and Florida. The 12-km ETo forecasts were produced by downscaling coarse-scale ETo forecasts from the CFSv2 retrospective forecast archive and by downscaling CFSv2 maximum temperature (Tmax), minimum temperature (Tmin), mean temperature (Tmean), solar radiation (Rs), and wind speed (Wind) individually and calculating ETo using those downscaled variables. All the ETo forecasts were calculated using the Penman–Monteith equation. Sensitivity coefficients were evaluated to quantify how and how much does each of the variables influence ETo. Two statistical downscaling methods were tested: 1) spatial disaggregation (SD) and 2) spatial disaggregation with quantile mapping bias correction (SDBC). The downscaled ETo from the coarse-scale ETo showed similar skill to those by first downscaling individual variables and then calculating ETo. The sensitivity coefficients showed Tmax and Rs had the greatest influence on ETo, followed by Tmin and Tmean, and Wind. The downscaled Tmax showed highest predictability, followed by Tmean, Tmin, Rs, and Wind. SDBC had slightly better performance than SD for both probabilistic and deterministic forecasts. The skill was locally and seasonally dependent. The CFSv2-based ETo forecasts showed higher predictability in cold seasons than in warm seasons. The CFSv2 model could better predict ETo in cold seasons during El Niño–Southern Oscillation (ENSO) events only when the forecast initial condition was in either the El Niño or La Niña phase of ENSO.


2013 ◽  
Vol 118 (3) ◽  
pp. 1312-1328 ◽  
Author(s):  
Xingwen Jiang ◽  
Song Yang ◽  
Yueqing Li ◽  
Arun Kumar ◽  
Wanqiu Wang ◽  
...  

2020 ◽  
Author(s):  
Kristina Fröhlich ◽  
Mikhail Dobrynin ◽  
Katharina Isensee ◽  
Claudia Gessner ◽  
Andreas Paxian ◽  
...  

2018 ◽  
Vol 18 (18) ◽  
pp. 13547-13579 ◽  
Author(s):  
Zachary D. Lawrence ◽  
Gloria L. Manney ◽  
Krzysztof Wargan

Abstract. We compare herein polar processing diagnostics derived from the four most recent “full-input” reanalysis datasets: the National Centers for Environmental Prediction Climate Forecast System Reanalysis/Climate Forecast System, version 2 (CFSR/CFSv2), the European Centre for Medium-Range Weather Forecasts Interim (ERA-Interim) reanalysis, the Japanese Meteorological Agency's 55-year (JRA-55) reanalysis, and the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). We focus on diagnostics based on temperatures and potential vorticity (PV) in the lower-to-middle stratosphere that are related to formation of polar stratospheric clouds (PSCs), chlorine activation, and the strength, size, and longevity of the stratospheric polar vortex. Polar minimum temperatures (Tmin) and the area of regions having temperatures below PSC formation thresholds (APSC) show large persistent differences between the reanalyses, especially in the Southern Hemisphere (SH), for years prior to 1999. Average absolute differences of the reanalyses from the reanalysis ensemble mean (REM) in Tmin are as large as 3 K at some levels in the SH (1.5 K in the Northern Hemisphere – NH), and absolute differences of reanalysis APSC from the REM up to 1.5 % of a hemisphere (0.75 % of a hemisphere in the NH). After 1999, the reanalyses converge toward better agreement in both hemispheres, dramatically so in the SH: average Tmin differences from the REM are generally less than 1 K in both hemispheres, and average APSC differences less than 0.3 % of a hemisphere. The comparisons of diagnostics based on isentropic PV for assessing polar vortex characteristics, including maximum PV gradients (MPVGs) and the area of the vortex in sunlight (or sunlit vortex area, SVA), show more complex behavior: SH MPVGs showed convergence toward better agreement with the REM after 1999, while NH MPVGs differences remained largely constant over time; differences in SVA remained relatively constant in both hemispheres. While the average differences from the REM are generally small for these vortex diagnostics, understanding such differences among the reanalyses is complicated by the need to use different methods to obtain vertically resolved PV for the different reanalyses. We also evaluated other winter season summary diagnostics, including the winter mean volume of air below PSC thresholds, and vortex decay dates. For the volume of air below PSC thresholds, the reanalyses generally agree best in the SH, where relatively small interannual variability has led to many winter seasons with similar polar processing potential and duration, and thus low sensitivity to differences in meteorological conditions among the reanalyses. In contrast, the large interannual variability of NH winters has given rise to many seasons with marginal conditions that are more sensitive to reanalysis differences. For vortex decay dates, larger differences are seen in the SH than in the NH; in general, the differences in decay dates among the reanalyses follow from persistent differences in their vortex areas. Our results indicate that the transition from the reanalyses assimilating Tiros Operational Vertical Sounder (TOVS) data to advanced TOVS and other data around 1998–2000 resulted in a profound improvement in the agreement of the temperature diagnostics presented (especially in the SH) and to a lesser extent the agreement of the vortex diagnostics. We present several recommendations for using reanalyses in polar processing studies, particularly related to the sensitivity to changes in data inputs and assimilation. Because of these sensitivities, we urge great caution for studies aiming to assess trends derived from reanalysis temperatures. We also argue that one of the best ways to assess the sensitivity of scientific results on polar processing is to use multiple reanalysis datasets.


2013 ◽  
Vol 42 (7-8) ◽  
pp. 1925-1947 ◽  
Author(s):  
J. S. Chowdary ◽  
H. S. Chaudhari ◽  
C. Gnanaseelan ◽  
Anant Parekh ◽  
A. Suryachandra Rao ◽  
...  

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