Search Results
, using the monthly average SST and SHF data at the base grid of 0.25°. Here we adopt a negative lag as SST leading SHF. We will be guided by the stochastic air–sea interaction model in Eqs. (1) and (2) and structure of the lead–lag correlation in Fig. 1 to determine globally where oceanic and atmospheric weather lead to ocean- versus atmosphere-driven variability. As a metric to assess the scale dependence of the lagged correlation of SST–SHF and SST tendency–SHF we define a transition length
, using the monthly average SST and SHF data at the base grid of 0.25°. Here we adopt a negative lag as SST leading SHF. We will be guided by the stochastic air–sea interaction model in Eqs. (1) and (2) and structure of the lead–lag correlation in Fig. 1 to determine globally where oceanic and atmospheric weather lead to ocean- versus atmosphere-driven variability. As a metric to assess the scale dependence of the lagged correlation of SST–SHF and SST tendency–SHF we define a transition length
primary water mass that interacts with the atmosphere in key regions of air–sea interaction such as the Gulf Stream and its recirculation. It is generally accepted that mode waters are formed in the process of winter vertical convection, which is triggered by heat loss from the ocean to the atmosphere, and so can be modulated by, for example, the North Atlantic Oscillation (NAO) (see Marsh and New 1996 ). However, when analyzing data from the Panulirus station, Jenkins (1982) obtained a poor
primary water mass that interacts with the atmosphere in key regions of air–sea interaction such as the Gulf Stream and its recirculation. It is generally accepted that mode waters are formed in the process of winter vertical convection, which is triggered by heat loss from the ocean to the atmosphere, and so can be modulated by, for example, the North Atlantic Oscillation (NAO) (see Marsh and New 1996 ). However, when analyzing data from the Panulirus station, Jenkins (1982) obtained a poor
dynamics and land–atmosphere interaction, air–sea interaction may also play an important role in the MISO amplitude and its propagation. Krishnamurti et al. (1988) found evident 30–50-day oscillations of sea surface temperature (SST) over the Bay of Bengal (BoB), which were attributed to surface heat flux anomalies. Coherent relationships between intraseasonal variabilities of SST, precipitation, and associated winds were later confirmed by field observations ( Bhat et al. 2001 ; Webster et al. 2002
dynamics and land–atmosphere interaction, air–sea interaction may also play an important role in the MISO amplitude and its propagation. Krishnamurti et al. (1988) found evident 30–50-day oscillations of sea surface temperature (SST) over the Bay of Bengal (BoB), which were attributed to surface heat flux anomalies. Coherent relationships between intraseasonal variabilities of SST, precipitation, and associated winds were later confirmed by field observations ( Bhat et al. 2001 ; Webster et al. 2002
1. Introduction El Niño–Southern Oscillation (ENSO) is a mode of air–sea interaction in the equatorial Pacific with profound influences on the global climate. For example, El Niño causes sea surface temperature (SST) to increase over the tropical Indian Ocean (TIO) with a one-season lag ( Klein et al. 1999 ; Lau and Nath 2000 , 2003 ; Alexander et al. 2002 ). This basin-wide warming pattern is so robust that it is detected from sparse ship observations in an early study of Weare (1979) . It
1. Introduction El Niño–Southern Oscillation (ENSO) is a mode of air–sea interaction in the equatorial Pacific with profound influences on the global climate. For example, El Niño causes sea surface temperature (SST) to increase over the tropical Indian Ocean (TIO) with a one-season lag ( Klein et al. 1999 ; Lau and Nath 2000 , 2003 ; Alexander et al. 2002 ). This basin-wide warming pattern is so robust that it is detected from sparse ship observations in an early study of Weare (1979) . It
-LR are excluded in the lead years averaged over 1–10 years due to a lack of data in the first lead year. Although the PDO mechanisms are still under debate ( Mantua and Hare 2002 ), it is widely accepted that the midlatitude air–sea interaction (ASI) over the North Pacific and tropical–midlatitude interactions play important roles in PDO formation, maintenance, and phase transitions ( Latif and Barnett 1994 , 1996 ; Barnett et al. 1999 ; Meehl and Hu 2006 ; Fang and Yang 2011 ; Farneti et
-LR are excluded in the lead years averaged over 1–10 years due to a lack of data in the first lead year. Although the PDO mechanisms are still under debate ( Mantua and Hare 2002 ), it is widely accepted that the midlatitude air–sea interaction (ASI) over the North Pacific and tropical–midlatitude interactions play important roles in PDO formation, maintenance, and phase transitions ( Latif and Barnett 1994 , 1996 ; Barnett et al. 1999 ; Meehl and Hu 2006 ; Fang and Yang 2011 ; Farneti et
associated MISO events, by identifying the biases for 28 active and 27 break spells in CFSv2 forecasts during 1999–2010. Then, we investigate the causes for the biases, by diagnosing intraseasonal air–sea coupling processes in CFSv2. A linear local air–sea interaction model is employed to examine the key processes misrepresented in the model. The rest of the paper is organized as follows. Section 2 introduces observational and reanalysis datasets and CFSv2 forecasts. Section 3 describes the selection
associated MISO events, by identifying the biases for 28 active and 27 break spells in CFSv2 forecasts during 1999–2010. Then, we investigate the causes for the biases, by diagnosing intraseasonal air–sea coupling processes in CFSv2. A linear local air–sea interaction model is employed to examine the key processes misrepresented in the model. The rest of the paper is organized as follows. Section 2 introduces observational and reanalysis datasets and CFSv2 forecasts. Section 3 describes the selection
1. Introduction The impact of the air–sea interaction on the behavior of the Madden–Julian oscillation (MJO; Madden and Julian 1971 ) has been a major area of research interest particularly since observations from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) showed that sea surface temperature (SST) in the Indo-Pacific warm pool is modulated by the passage of the MJO (e.g., Weller and Anderson 1996 ; Hendon and Glick 1997
1. Introduction The impact of the air–sea interaction on the behavior of the Madden–Julian oscillation (MJO; Madden and Julian 1971 ) has been a major area of research interest particularly since observations from the Tropical Ocean and Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) showed that sea surface temperature (SST) in the Indo-Pacific warm pool is modulated by the passage of the MJO (e.g., Weller and Anderson 1996 ; Hendon and Glick 1997
the eastern equatorial Pacific SST anomalies is relatively weak during the ENSO transition phase, local air–sea interaction may be prominent in the WNP where the mean SST is high. Wang et al. (2000) proposed a positive thermodynamic feedback mechanism between atmospheric Rossby waves and the SST anomalies for sustaining anomalous Philippine Sea anticyclone during the decay of ENSO. This mechanism relies on a wind–evaporation feedback on SST. However, the roles of humidity and temperature changes
the eastern equatorial Pacific SST anomalies is relatively weak during the ENSO transition phase, local air–sea interaction may be prominent in the WNP where the mean SST is high. Wang et al. (2000) proposed a positive thermodynamic feedback mechanism between atmospheric Rossby waves and the SST anomalies for sustaining anomalous Philippine Sea anticyclone during the decay of ENSO. This mechanism relies on a wind–evaporation feedback on SST. However, the roles of humidity and temperature changes
roughness sublayer, all the surface roughness parameters for describing wave states are important for the WBL. Oceanic waves are commonly divided into wind wave or wind sea and swell; the former is generated by local wind and is characterized with high frequencies and the latter is generated by distant air–sea interactions and is characterized with low frequencies. Snodgrass et al. (1966) found that storm-generated swell can travel long distances, and interactions between swell and local wind is
roughness sublayer, all the surface roughness parameters for describing wave states are important for the WBL. Oceanic waves are commonly divided into wind wave or wind sea and swell; the former is generated by local wind and is characterized with high frequencies and the latter is generated by distant air–sea interactions and is characterized with low frequencies. Snodgrass et al. (1966) found that storm-generated swell can travel long distances, and interactions between swell and local wind is
1. Introduction The passage of a tropical cyclone (TC) over a warm ocean represents one of the most extreme cases of air–sea interaction. The most apparent effects of TC passage are marked sea surface temperature (SST) cooling of 1° to 5°C, strong current velocities of more than 2 m s −1 , and large surface gravity waves. It is well established that the intensity of a TC over an open ocean may be significantly affected by the cooling of SST caused by air–sea interaction ( Khain and Ginis 1991
1. Introduction The passage of a tropical cyclone (TC) over a warm ocean represents one of the most extreme cases of air–sea interaction. The most apparent effects of TC passage are marked sea surface temperature (SST) cooling of 1° to 5°C, strong current velocities of more than 2 m s −1 , and large surface gravity waves. It is well established that the intensity of a TC over an open ocean may be significantly affected by the cooling of SST caused by air–sea interaction ( Khain and Ginis 1991
