A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part I: Interannual variability

We investigate the contribution of the local and remote atmospheric moisture fluxes to East Asia (EA) precipitation and its interannual variability during 1979-2012. We use and expand the Brubaker et al. (J Clim 6:1077(J Clim 6: -1089(J Clim 6: ,1993) method, which connects the area-mean precipitation to area-mean evaporation and the horizontal moisture flux into the region. Due to its large landmass and hydrological heterogeneity, EA is divided into five sub-regions: Southeast (SE), Tibetan Plateau (TP), Central East (CE), Northwest (NW) and Northeast (NE). For each region, we first separate the contributions to precipitation of local evaporation from those of the horizontal moisture flux by calculating the precipitation recycling ratio: the fraction of precipitation over a region that originates as evaporation from the same region. Then, we separate the horizontal moisture flux across the region's boundaries by direction. We estimate the contributions of the horizontal moisture fluxes from each direction, as well as the local evaporation, to the mean precipitation and its interannual variability. We find that the major contributors to the mean precipitation are not necessarily those that contribute most to the precipitation interannual variability. Over SE, the moisture flux via the southern boundary dominates the mean precipitation and its interannual variability. Over TP, in winter and spring, the moisture flux via the western boundary dominates the mean precipitation; however, variations in local evaporation dominate the precipitation interannual variability. The western moisture flux is the dominant contributor to the mean precipitation over CE, NW and NE. However, the southern or northern moisture flux or the local evaporation dominates the precipitation interannual variability over these regions, depending on the season. Potential mechanisms associated with interannual variability in the moisture flux are identified for each region. The methods and results presented in this study can be readily applied to model simulations, to identify simulation biases in precipitation that relate to the simulated moisture supplies and transport.

show abstract

“…The source regions of the perturbation may vary from one occurrence of the wave pattern to another (Stephan et al 2017). …”

Section: Southern Boundariesmentioning

confidence: 99%

The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…7), negative OLR anomalies over the western equatorial Indian Ocean and positive OLR anomalies over the eastern equatorial Atlantic Ocean (not shown). Stephan et al (2017a) showed that the pressure distribution is consistent with a wavenumber-3 Rossby wave originating over central Africa or the Arabian Sea and propagating across China and Japan into the Pacific. 25 A pattern with a peak along the southeast coast is found in A216, C96, C216 and C512b.…”

Section: Patternmentioning

confidence: 76%

“…Based on APHRODITE data, Stephan et al (2017a) identified the dominant regions of observed coherent IAV in seasonal precipitation in China, and linked that variability to large-scale and regional mechanisms. They analyzed only EOTs that explained at least 5 % of the total space-time variance, which resulted in two patterns in DJF, JJA, SON, and three in MAM.…”

mentioning

confidence: 99%

Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

Stephan

Klingaman

Vidale

et al. 2017

Preprint

Self Cite

View full text Add to dashboard Cite

Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyze the sensitivity to air-sea coupling and horizontal resolution, atmosphereonly and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ∼200, 90, and 40 km in the zonal direction at the equator, respectively) are analyzed. The mean and interannual variance of seasonal pre-5 cipitation are too high in all simulations over China, but improve with finer resolution and coupling. Empirical OrthogonalTeleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled airsea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal-mean timeseries. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in 10 summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis 15 adds knowledge about coherent variability and associated mechanisms. Copyright statement. TEXT 1 Geosci. Model Dev. Discuss., https://doi.

show abstract

“…The EOT method has been successfully demonstrated for interannual precipitation variability (Smith 2004;Rotstayn et al 2010;Klingaman et al 2013;King et al 2014) and for intraseasonal extreme precipitation (King et al 2014). In Part I of this study (Stephan et al 2017) the method was shown to successfully reproduce known patterns of interannual precipitation variability in China. Restricting our analysis domain to China, the first EOT is defined as the timeseries at the point that explains the most variance in the area-averaged precipitation timeseries.…”

Section: Empirical Orthogonal Teleconnectionsmentioning

confidence: 98%

“…Empirical Orthogonal Teleconnection analysis (EOT) is applied to observed precipitation from the APHRODITE data set. We also use EOT analysis and APHRODITE in Part I of this study (Stephan et al 2017), where the focus is on interannual variability of seasonal precipitation. In Part I, EOT analysis successfully reproduces robust known patterns reported by several earlier studies; it also provides new insights into patterns of coherent precipitation variability and their causes.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part II: intraseasonal variability

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part I: Interannual variability

Cited by 31 publications

References 63 publications

The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability

The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability

Using Empirical Orthogonal Teleconnections to evaluate interannual rainfall variability over China in the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations

A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part II: intraseasonal variability

Contact Info

Product

Resources

About