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Climate change Air - Sea interactions Internal climate variability
East Asian monsoon Climate Modeling Climate Chemistry interactions

Fundamental 

▶ Internal atmospheric variability, also termed “climate noise” , arises from non-linear dynamical processes intrinsic to the atmosphere(Madden, 1976; Schneider and Kinter,1994; Feldstein, 2000; Deser et al., 2010).

 Internal variability is the natural variability of the climate system that occurs in the absence of external forcing (Deser et al., 2010). Although the atmosphere contains little memory beyond a few weeks, it exhibits long-time scale variability characteristic of a random stochastic process. Internal climate variability arises from atmospheric, oceanic, land, and cryospheric processes and their coupled interactions(Kay et al., 2014). 

 Uncertainty in future climate change derives from three main sources: forcing, model response, and internal variability (e.g., Hawkins and Sutton 2009; Tebaldi and Knutti 2007).

 All 30 CESM-LE members use the same model and the same external forcing. Each CESM-LE ensemble member has a unique climate trajectory due to small round-off level differences in their atmospheric initial conditions. (Kay et al., 2014).



Spread of Ensembles

 ▶ Standard deviation of the absolute values of differences between ensemble mean and 30 members. Simply put, the CESM-LE ensemble spread results from internally generated climate variability alone. Standard deviation of the absolute values of differences between ensemble mean and 30 members. Simply put, the CESM-LE ensemble spread results from internally generated climate variability alone.

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Fundamental 
 

  Previous studies suggest that it is reasonable to expect a direct relationship between sea surface temperature and deep convection cloud(SST-DCC), however, the relationship between the local SST and DCC over warm ocean SST> 27°C) is rather complex because of the influence of large-scale atmospheric dynamics that govern atmospheric convergence in the lower and middle troposphere and divergence aloft [Graham and Barnett, 1987; Waliser and Graham, 1993; Zhang, 1993; Meenu et al., 2012].

 

  Waliser et al. (1993) found that the characteristic of the relationship between SST and DCC is such that at temperature between 27 and 29°C, convection increases with increasing SST, but above 29°C(i.e. in the western tropical Pacific during boreal summer), the intensity of convection observed tends to decrease with increasing SST.

 

  That is, The SST-DCC correlation is highly positive in the central-to-eastern tropical Pacific, the core region of interannual variability associated with El Nino-Southern Oscillation (ENSO), On the other hand, the overall summer SST-DCC relationship has a negative correlation in the western tropical Pacific, this relationship experiences a significant interannual variation [R Wu and Kirtman, 2005; B Wu et al., 2009; Kumar et al., 2013]. 


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Result


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References

   Graham, N., and T. Barnett(1987), Sea surface temperature, surface wind divergence, and convection over  tropical oceans, Science,238(4827), 657-659.

   Kumar, A., M. Chen, and W.Wang (2013), Understanding prediction skill of seasonal mean precipitation over the tropics, J. Clim., 26(15), 5674-5681.

  Meenu,S., K. Parameswaran, and K. Rajeev (2012), Role of sea surface temperature and wind convergence in regulating convection over the tropical Indian Ocean,J. Geophys. Res. Atmos., 117.

   Waliser, D. E., and N. E.Graham (1993), Convective cloud systems and warmpool sea surface temperatures: Coupled interactions and selfregulation,J. Geophys. Res. Atmos., 98(D7), 12881-12893.

   Wu, B., T. Zhou, and T. Li(2009), Contrast of Rainfall-SST Relationships in the Western North Pacific between the ENSO- Developing and ENSO-Decaying Summers*, J. Clim., 22(16),4398-4405.

  Wu,R., and B. P. Kirtman (2005), Roles of Indian and Pacific Ocean air–sea coupling in tropical atmospheric variability,  Clim. Dyn., 25(2-3),155-170.

  Zhang,C. (1993), Large-scale variability of atmospheric deep convection in relation to sea surface temperature in the tropics, J. Clim., 6(10), 1898-1913.


 




Fundamental


▶ Climate Chemistry interactions, particularly “Short lived climate pollutants(SLCPs)” such as Sulfate aerosols, Black carbon etc. play a role to influence the weather and climate variability by changing radiative forcings (IPCC 2007).

 

▶ One is direct effect in which particles scatter and absorb the solar and terrestrial radiation. The other is an indirect effect in which they change  the microphysical and optical properties of cloud droplets responding that cloud condensation nuclei (Twomey, 1974). 


▶ According to recent studies, the short lived climate pollutants play a role in influencing the precipitation variability by changing the properties of the clouds, such as the cloud optical depth, the cloud droplet size, and the vertical distribution of the cloud droplets within the clouds

    (Hansen et al.,2007; Breon et al., 2002; Feingold et al., 2003; Tang et al., 2014).



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Figure  1. Schematic diagram showing the various radiative mechanisms associated with cloud effects that have been identified as significant in relation to aerosols.(Jasper Kirkby, 2009)


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Figure  2. Global-average radiative forcing (RF) estimates and ranges in 2005 for anthropogenic carbon dioxide(CO2), methane (CH4),  nitrousoxide (N2O) and other important agents and mechanisms(IPCC, 2007).











Fundamental


 The MOM4 is a numerical ocean model based on the hydrostatic primitive equations.The MOM4 is configured with 50 vertical levels (22 levels of 10-m thickness in the top 220m), 1 ° longitude by 1° latitudinal spacing near the equator. The model has an explicit surface with freshwater fluxes exchanged between the atmosphere and ocean [S. Zhang et al. 2010].


 Parameterized physical processes include K-profile parameterization (KPP) vertical mixing,neutral physics, a spatially dependent anisotropic viscosity, and a shortwave radiative penetration depth that depends on a prescribed climatological ocean color. Insolation varies diurnally and the wind stress at the ocean surface is computed using the velocity of the wind relative to surface currents. An efficient time stepping scheme is employed [Griffies et al. 2005].




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Figure 1. Schematic diagram of elements in the NCAR coupledocean-atmosphere general circulation climate model. [Gerald A Meehl, 1989]




The Pacific SST variability and the NINO SST index in late-1990s were changed result from the 1998/99 regime shift[Jo et al. 2014] , as well as the mean zonal SST difference signal changed in 1999 [Chung et al. 2013].



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Figure 2. Standard deviations of the inter-annual SSTA averaged from September to the following February. [Chung et al, 2013]

 


Results

Figure 3. Global of use the MOM.4 model. The inter-annual sea surface temperature anomalies during boreal winter over the periods of 1979-2009. In figure 2, NINO3.4 SSTA indexes area is latitudes from 5˚N to 5˚S and longitudes from 190˚E to 240˚E. Other indexes (observation data) are during same period time series and same area.

 


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Figure 3. NINO3.4 SSTA indexes are used observation data and model result. Observations are SODA (violet),ERSST (green) and HadiSST (orange). MOM.4 results are used om3 core data (red)and ECMWF data (blue).




Reference

 Breon FM, Tanre D, and Generoso S, 2002. Aerosol effect on cloud droplet size monitored from satellite. Science  295:  834-838. 

 Change IPCC 2007. Climate change 2007: The physical science basis.Agenda  6: 333.. 

 Feingold G, Eberhard WL, Veron DE, and Previdi M, 2003. Firstmeasurements of the Twomey indirect effect using groundbased remote sensors. GeophysicalResearch Letters  30.

 Hansen J, Sato M, Kharecha P, Russell G, Lea DW, and Siddall M, 2007.Climate change and trace gases. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 365: 1925-1954.

 Jasper Kirkby, 2009. Cosmic rays and climate .CERN Colloquium

 Tang J, and Coauthors, 2014. Positive relationship between liquid cloud droplet effective radius and aerosol optical depth over  Eastern China from satellite data.Atmospheric Environment  84: 244-253. 

Trenberth KE, 2011. Changes in precipitation with climate change. Climate Research  47: 123. 

 Twomey S, 1977. The influence of pollution on the shortwave albedo of clouds. Journal of the atmospheric sciences  34: 1149-1152.


Fundamental


 The main reason of the global surface warming is the changes of mean states and long-term variability in ocean and atmosphere by the greenhouse gas increase. Therefore, understanding the mechanism what induces the changes of atmosphere and ocean, is important. 


 The ocean, which has the highest heat capacity in the climate system, has experienced significant change in global heat content over the past 40 years [levitus et al, 2000]. To understand the ocean’s role in climate system, the ocean’s ability to store and transportheat has been studied in several researches [levitus et al, 2000, 2005, 2009;Gouretski and Koltermann, 2007; Domingues et al, 2008], in association with theocean’s significant contribution to global warming and climate change [Houghtonet al, 1996].


 The recent sea surface temperature (SST) of marginal sea becomes much warmer than the SST of a global SST [Yeh and Kim, 2012; Lima and David, 2012]. The larger SST is associated with northerly wind in Yellow sea and East China Sea. This results in the changes in the wind flow in western Pacific Ocean along with the change in the precipitation in East China and Korea peninsula. It indicates that the marginal SST can contribute on atmospheric variability. In addition, warming in Yellow Sea is associated with in the warming in East China Sea and South China Sea.


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Meehl et al (2007)

Figure 1. Annual mean surface temperature (GMST) anomalies relative to a 1961-1990 climatology from the latest version of the three combined land-surface air temperature (LSAT) and sea surface temperature (SST) data sets.


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Lima and David (2012)
Figure 2. Changes in temperature along the world’s coast lines. Warming rates for the period between 1982 and 2010, expressed in ℃ per decade. Red indicates warming and blue indicates cooling






































































































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Fundamental


▶ The East Asian summer monsoon (EASM) is one of the most important and active components of the global climate system (Ding 1994; Chang 2004; Chang et al. 2011). On the interannual time scale, it has been demonstrated by previous studies that variablilty of the EASM is influenced by many factors, such as Eurasian snow cover (Wu and Qian 2003; Wu and Kirtman 2007; Zhao et al. 2007; Wu et al. 2009a), western Pacific subtropical high (Chang et al. 2000; Lu 2002), El Nino-Southern Oscillation (ENSO) cycle (Wang et al. 2000; Wu et al. 2003; Huang et al. 2004l Chen et al. 2013), western Pacific warm pool (Nitta 1987; Huang and Li 1987; Huang and Sun 1992), Indian summer monsoon (Wu 2002), and spring Arctic sea ice concentration (Wu et al. 2009b) 

▶ The East Asian monsoon features strong southerly winds and abundant rainfall in summer a strong northerly winds and little rainfall in winter. Strong southerly flow in summer brings a large amount of water vapor from the tropical western Pacific and the Indian Ocean to eastern China, Japan, and the Korean Peninsula, causing continuous and heavy rainfall in these regions (Tao and Chen 1987; ding 1994; Chang et al. 2000a; Chen et al. 2009). 

▶ The interannual variability of EASM is affected by anomalous states of lower boundary condition such as sea surface (SST), snow cover/snow depth, and soil moisture (Charney and Shukla, 1981). Among them, the snow cover/snow depth may have an importan effect on the interannual variability of the monsoon because of its ability to alter the surface albedo and to regulate the soil moisture (Hahn and Shukla 1976; Barnett et al. 1989; Yasunari et al. 1991; Sankar-Rao et al. 1996; Kripalani et al. 2003). 

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Figure.1 Regression patterns of seasonal-mean 850-hPa winds in (a) DJF, (b) 1MAM, and (c) 1JJA with respect to EAWMI. The plus symbol, 1, stands for the season following winter. Shading indicates the 90% confidence level according to a two-tailed Student’s t test. (Chen et al. 2013) 


Reference 

▶ Chang, Chih-Pei. East Asian Monsoon. Vol. 2. World Scientific, 2004.
▶ Chang, Chih-Pei. The global monsoon system: research and forecast. Vol. 5. World Scientific, 2011.
▶ Chen, Shangfeng, Wen Chen, and Renguang Wu. "An interdecadal change in the relationship between boreal spring Arctic
    Oscillation and the East Asian Summer Monsoon around the early 1970s." Journal of Climate 28.4 (2015): 1527-1542. 
▶ Chen, Wen, Juan Feng, and Renguang Wu. "Roles of ENSO and PDO in the link of the East Asian winter monsoon to 
   the following summer monsoon."Journal of Climate 26.2 (2013): 622-635. 
▶ Ding Yihui. Monsoons over china. Vol. 16. Springer Science & Business Media, 1994. 
▶ Yim, So‐Young, et al. "Two distinct patterns of spring Eurasian snow cover anomaly and their impacts on the East Asian
   summer monsoon." Journal of Geophysical Research: Atmospheres (1984–2012) 115.D22 (2010).