Large-Scale Variability of Atmospheric Deep Convection in Relation to Sea Surface Temperature in the Tropics

1993 ◽  
Vol 6 (10) ◽  
pp. 1898-1913 ◽  
Author(s):  
Chidong Zhang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Deep Convection ◽  
Sea Surface ◽  
The Tropics

scholarly journals Sea Surface Temperature Warming Patterns and Future Vegetation Change

2015 ◽  
Vol 28 (20) ◽  
pp. 7943-7961 ◽  
Author(s):  
Sara A. Rauscher ◽  
Xiaoyan Jiang ◽  
Allison Steiner ◽  
A. Park Williams ◽  
D. Michael Cai ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Vegetation Change ◽  
Tree Mortality ◽  
Ensemble Member ◽  
Sea Surface ◽  
Community Land Model ◽  
Sst Warming ◽  
The Tropics

Abstract Recent modeling studies of future vegetation change suggest the potential for large-scale forest die-off in the tropics. Taken together with observational evidence of increasing tree mortality in numerous ecosystem types, there is clearly a need for projections of vegetation change. To that end, the authors have performed an ensemble of climate–vegetation experiments with the National Science Foundation–DOE Community Atmosphere Model (CAM) coupled to the Community Land Model (CAM–CLM-CN) with its dynamic vegetation model enabled (CAM–CLM-CNDV). To overcome the limitations of using a single model, the authors employ the sea surface temperature (SST) warming patterns simulated by eight different models from the Coupled Model Intercomparison Program phase 3 (CMIP3) as boundary conditions. Since the SST warming pattern in part dictates how precipitation may change in the future, in this way a range of future vegetation–climate trajectories can be produced. On an annual average basis, this study’s CAM–CLM-CN simulations do not produce as large a spread in projected precipitation as the original CMIP3 archive. These differences are due to the tendency of CAM–CLM-CN to increase tropical precipitation under a global warming scenario, although this response is modulated by the SST warming patterns imposed. However, the CAM–CLM-CN simulations reproduce the enhanced dry season in the tropics simulated by CMIP3. These simulations show longer fire seasons and increases in fractional area burned. In one ensemble member, extreme droughts over tropical South America lead to fires that remove vegetation cover in the eastern Amazon, suggesting that large-scale die-offs are an unlikely but still possible event.

scholarly journals BCC-CSM2-HR: A High-Resolution Version of the Beijing Climate Center Climate System Model

2020 ◽  
Author(s):  
Tongwen Wu ◽  
Rucong Yu ◽  
Yixiong Lu ◽  
Weihua Jie ◽  
Yongjie Fang ◽  
...  
Keyword(s):  
High Resolution ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Climate System ◽  
Sea Surface ◽  
System Model ◽  
Quasi Biennial Oscillation ◽  
Climate System Model ◽  
Medium Resolution ◽  
The Tropics

Abstract. BCC-CSM2-HR is a high-resolution version of the Beijing Climate Center (BCC) Climate System Model. Its development is on the basis of the medium-resolution version BCC-CSM2-MR which is the baseline for BCC participation to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study documents the high-resolution model, highlights major improvements in the representation of atmospheric dynamic core and physical processes. BCC-CSM2-HR is evaluated for present-day climate simulations from 1971 to 2000, which are performed under CMIP6-prescribed historical forcing, in comparison with its previous medium-resolution version BCC-CSM2-MR. We focus on basic atmospheric mean states over the globe and variabilities in the tropics including the tropic cyclones (TCs), the El Niño–Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and the quasi-biennial oscillation (QBO) in the stratosphere. It is shown that BCC-CSM2-HR keeps well the global energy balance and can realistically reproduce main patterns of atmosphere temperature and wind, precipitation, land surface air temperature and sea surface temperature. It also improves in the spatial patterns of sea ice and associated seasonal variations in both hemispheres. The bias of double intertropical convergence zone (ITCZ), obvious in BCC-CSM2-MR, is almost disappeared in BCC-CSM2-HR. TC activity in the tropics is increased with resolution enhanced. The cycle of ENSO, the eastward propagative feature and convection intensity of MJO, the downward propagation of QBO in BCC-CSM2-HR are all in a better agreement with observation than their counterparts in BCC-CSM2-MR. We also note some weakness in BCC-CSM2-HR, such as the excessive cloudiness in the eastern basin of the tropical Pacific with cold Sea Surface Temperature (SST) biases and the insufficient number of tropical cyclones in the North Atlantic.

scholarly journals Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

2010 ◽  
Vol 23 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Liew Juneng ◽  
Fredolin T. Tangang ◽  
Hongwen Kang ◽  
Woo-Jin Lee ◽  
Yap Kok Seng
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Winter Season ◽  
Peninsular Malaysia ◽  
Sea Surface ◽  
Asia Pacific ◽  
Forecast Data ◽  
Multimodel Ensembles

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

scholarly journals Assessment of the Sea Surface Temperature Predictability Based on Multimodel Hindcasts

2019 ◽  
Vol 34 (6) ◽  
pp. 1965-1977 ◽  
Author(s):  
Shouwen Zhang ◽  
Hua Jiang ◽  
Hui Wang
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Forecast Accuracy ◽  
Skill Score ◽  
Point Of View ◽  
Sea Surface ◽  
Lead Times ◽  
Spring Predictability Barrier ◽  
Multimodel Ensembles ◽  
The Tropics

Abstract Based on historical forecasts of four individual forecasting systems, we conducted multimodel ensembles (MME) to predict the sea surface temperature anomaly (SSTA) variability and assessed these methods from a deterministic and probabilistic point of view. To investigate the advantages and drawbacks of different deterministic MME methods, we used simple averaged MME with equal weighs (SCM) and the stepwise pattern projection method (SPPM). We measured the probabilistic forecast accuracy by Brier skill score (BSS) combined with its two components: reliability (Brel) and resolution (Bres). The results indicated that SCM showed a high predictability in the tropical Pacific Ocean, with a correlation exceeding 0.8 with a 6-month lead time. In general, the SCM outperformed the SPPM in the tropics, while the SPPM tend to show some positive effect on the correction when at long lead times. Corrections occurred for the spring predictability barrier of ENSO, in particular for improvements when the correlation was low or the RMSE was large using the SCM method. These qualitative results are not susceptible to the selection of the hindcast periods, it is as a rule rather by chance of these individual systems. Performance of our probabilistic MME was better than the Climate Forecast System version2 (CFSv2) forecasts in forecasting COLD, NEUTRAL, and WARM SSTA categories for most regions, mainly due to the contribution of Brel, indicating more adequate ensemble construction strategies of the MME system superior to the CFSv2.

scholarly journals Adjusting for Desert-Dust-Related Biases in a Climate Data Record of Sea Surface Temperature

2020 ◽  
Vol 12 (16) ◽  
pp. 2554
Author(s):  
Christopher J. Merchant ◽  
Owen Embury
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Large Scale ◽  
Dust Aerosol ◽  
Sea Surface ◽  
Climate Data ◽  
Desert Dust ◽  
Data Record ◽  
Climate Data Record

Atmospheric desert-dust aerosol, primarily from north Africa, causes negative biases in remotely sensed climate data records of sea surface temperature (SST). Here, large-scale bias adjustments are deduced and applied to the v2 climate data record of SST from the European Space Agency Climate Change Initiative (CCI). Unlike SST from infrared sensors, SST measured in situ is not prone to desert-dust bias. An in-situ-based SST analysis is combined with column dust mass from the Modern-Era Retrospective analysis for Research and Applications, Version 2 to deduce a monthly, large-scale adjustment to CCI analysis SSTs. Having reduced the dust-related biases, a further correction for some periods of anomalous satellite calibration is also derived. The corrections will increase the usability of the v2 CCI SST record for oceanographic and climate applications, such as understanding the role of Arabian Sea SSTs in the Indian monsoon. The corrections will also pave the way for a v3 climate data record with improved error characteristics with respect to atmospheric dust aerosol.

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