The E1 Nifio-Southern Oscillation （ENSO） is emphasized the roles of wind stress and heat flux environmental forcing to the ocean; its effect and modulated by many factors; most previous studies have in the tropical Pacific. Freshwater flux （FWF） is another the related ocean salinity variability in the ENSO region have been of increased interest recently. Currently, accurate quantifications of the FWF roles in the climate remain challenging; the related observations and coupled ocean-atmosphere modeling involve large elements of uncertainty. In this study, we utilized satellite-based data to represent FWF-induced feedback in the tropical Pacific climate system; we then incorporated these data into a hybrid coupled ocean-atmosphere model （HCM） to quantify its effects on ENSO. A new mechanism was revealed by which interannual FWF forcing modulates ENSO in a significant way. As a direct forcing, FWF exerts a significant influence on the ocean through sea surface salinity （SSS） and buoyancy flux （QB） in the western-central tropical Pacific. The SSS perturbations directly induced by ENSO-related interannual FWF variability affect the stability and mixing in the upper ocean. At the same time, the ENSO-induced FWF has a compensating effect on heat flux, acting to reduce interannual Qs variability during ENSO cycles. These FWF-induced processes in the ocean tend to modulate the vertical mixing and entrainment in the upper ocean, enhancing cooling during La Nifia and enhancing warming during E1 Nifio, respectively. The interannual FWF forcing-induced positive feedback acts to enhance ENSO amplitude and lengthen its time scales in the tropical Pacific coupled climate system.
ABSTRACT In this paper, interannual variations in the barrier layer thickness （BLT） are analyzed using Argo three-dimensional temperature and salinity data, with a locus on the effects of interannually varying salinity on the evolution of the El Nifio Southern Oscillation （ENSO）. The interannually varying BLT exhibits a zonal seesaw pattern across the equatorial Pacific during ENSO cycles. This phenomenon has been attributed to two different physical processes. During E1 Nifio （La Nifia）, the barrier layer （BL） is anomalously thin （thick） west of about 160°E, and thick （thin） to the east. In the western equatorial Pacific （the western part： 130°-160°E）, interannual variations of the BLT indicate a lead of one year relative to those of the ENSO onset. The interannual variations of the BLT can be largely attributed to the interannual temperature variability, through its dominant effect on the isothermal layer depth （ILD）. However, in the central equatorial Pacific （the eastern part： 160~E- 170~W）, interannual variations of the BL almost synchronously vary with ENSO, with a lead of about two months relative to those of the local SST. In this region, the interannual variations of the BL are significantly affected by the interannually varying salinity, mainly through its modulation effect on the mixed layer depth （MLD）. As evaluated by a onedimensional boundary layer ocean model, the BL around the dateline induced by interannual salinity anomalies can significantly affect the temperature fields in the upper ocean, indicating a positive feedback that acts to enhance ENSO.
In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model.The COF can also offset the impact of model error caused by stochastic processes.In reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions.Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results.In summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results.Therefore,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.
Using observations and reanalysis data, this study investigates the interannual relationship between the winter Aleutian Low(AL) and the rainfall anomalies in the following summer in South China(SC). Results show that the winter AL is significantly positively(negatively) correlated with the SC rainfall anomalies in the following July(August). Specifically, SC rainfall anomalies have a tendency to be positive(negative) in July(August) when the preceding winter AL is stronger than normal. The winter AL-related atmospheric circulation anomalies in the following summer are also examined. When the winter AL is stronger, there is a significant anticyclonic(cyclonic) circulation anomaly over the subtropical western North Pacific in the following July(August). Southerly(northerly) wind anomalies to the west of this anomalous anticyclonic(cyclonic) circulation increase(decrease) the northward moisture transportation and contribute to the positive(negative) rainfall anomalies over SC in July(August). This study indicates that the AL in the preceding winter can be used as a potential predictor of the rainfall anomalies in the following July and August over SC.
Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to the well-known Zebiak-Cane model, we re-simulate the 1997 and 2004 E1 Nifio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean com- ponent. It is found that the Zebiak-Cane model with the COF approach roughly reproduced the 1997 E1 Nifio, but the 2004 E1 Nifio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-E1 Nifio or EP-E1 Nifio. In hoth E1 Nifio simulations, substituting the COF with the OFV improved the fit between the simulations and obser- vations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled E1 Nifio events even when the observational data （and hence the computational time） were reduced. Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs.
Paleoclimate simulations of the mid-Holocene （MH） and Last Glacial maximum （LGM） by the latest versions of the Flexible Global Ocean-Atmosphere-Land System model, Spectral Version 2 and Grid-point Version 2 （FGOALS-s2 and g2） are evaluated in this study. The MH is characterized by changes of insolation induced by orbital parameters, and the LGM is a glacial period with large changes in greenhouse gases, sea level and ice sheets. For the MH, both versions of FGOALS simulate reasonable responses to the changes of insolation, such as the enhanced summer monsoon in African-Asian regions. Model differences can be identified at regional and seasonal scales. The global annual mean surface air temperature （TAS） shows no significant change in FGOALS-s2, while FGOALS-g2 shows a global cooling of about 0.7~C that is related with a strong cooling during boreal winter. The amplitude of ENSO is weaker in FGOALS-g2, which agrees with proxy data. For the LGM, FGOALS-g2 captures the features of the cold and dry glacial climate, including a global cooling of 4.6℃ and a decrease in precipitation by 10%. The ENSO is weaker at the LGM, with a tendency of stronger ENSO cold events. Sensitivity analysis shows that the Equilibrium Climate Sensitivity （ECS） estimated for FGOALS ranges between 4.23℃ and 4.59℃. The sensitivity of precipitation to the changes of TAS is -2.3%℃-1, which agrees with previous studies. FGOALS-g2 shows better simulations of the Atlantic Meridional Overturning Circulation （AMOC） and African summer monsoon precipitation in the MH when compared with FGOALS-gl.0; however, it is hard to conclude any improvements for the LGM.
A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.
In this study, the sensitivities of net primary production(NPP), soil carbon, and vegetation carbon to precipitation and temperature variability over China are discussed using the state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model(LPJ DGVM). The impacts of the sensitivities to precipitation variability and temperature variability on NPP, soil carbon, and vegetation carbon are discussed. It is shown that increasing precipitation variability, representing the frequency of extreme precipitation events, leads to losses in NPP, soil carbon, and vegetation carbon over most of China, especially in North and Northeast China where the dominant plant functional types(i.e., those with the largest simulated areal cover) are grass and boreal needle-leaved forest. The responses of NPP, soil carbon, and vegetation carbon to decreasing precipitation variability are opposite to the responses to increasing precipitation variability. The variations in NPP, soil carbon, and vegetation carbon in response to increasing and decreasing precipitation variability show a nonlinear asymmetry. Increasing precipitation variability results in notable interannual variation of NPP. The sensitivities of NPP, soil carbon, and vegetation carbon to temperature variability, whether negative or positive, meaning frequent hot and cold days, are slight. The present study suggests, based on the LPJ model, that precipitation variability has a more severe impact than temperature variability on NPP, soil carbon, and vegetation carbon.
