APEC 기후센터(APCC)의 6명의 연구원들이 2013년 6월 24일에서 28일에 제주도에서 개최되는 제12회 통계기후학 국제학술회의(12th International Meeting on Statistical Climatology, IMSC)에 참석하여 발표를 진행한다. 참석 연구원들은 발표를 통해 대기 과학 및 통계학의 교차되는 부분에 대한 정보를 교환하고 본인의 연구 결과물을 공유한다.
 

발표자 및 발표내용과 초록은 아래에서 확인할 수 있다.
 

▶ Presenter: Dr. Erik Swenson of the Climate Prediction Team

▶ Title: Interaction between the AO and ENSO Modoki and Implications for Seasonal Prediction

▶ Abstract:

Past observational and modeling studies have demonstrated a link between tropical Pacific Sea Surface Temperature (SST) associated with ENSO Modoki and the Arctic Oscillation (AO) of which causality and role for seasonal prediction is still not well understood. During boreal winter, the AO has a tendency to precede changes in the tropics associated with ENSO Modoki implying a degree of two-‐way interaction that is ignored under the paradigm that such teleconnections arise purely as tropically‐ forced response patterns. Despite constraints of their own, multivariate statistical methods such as Maximum Covariance Analysis (MCA) allow for a more objective isolation of such observed relationships, and more importantly separation from conventional ENSO. In this study, the AO-‐ENSO Modoki relationship is investigated with a new statistical technique involving co‐variability between 500 hPa geopotential height and tropical precipitation. In a similar manner, the relationship is examined as a diagnostic in 10 CGCM ensemble hindcast datasets of the APEC Climate Center. Consistency between model representation and prediction skill/reliability is examined, and implications for predictability and real-‐time prediction are discussed.

▶ Presenter: Dr. Hongwei Yang of the Climate Analysis Team

▶ Title: Reduction of uncertainties in regional climate downscaling through ensemble forcing

▶ Abstract:

The atmospheric branch of the hydrological cycle associated with the East Asian summer monsoon is intricate, due to its distinct land-sea configurations: the highest mountains are to the west, the oceans are to the south and east, and mid-latitude influences come from the north. Remarkable differences are yielded in dynamical downscaling of the 1998 East Asian summer monsoon with the Weather Research and Forecast (WRF) model forced by NCEP-R2 and ERA-40. The differences are primarily caused by uncertainties in the water vapor influx across the lateral boundaries over the Bay of Bengal and the Philippine Sea in the reanalyses. The seasonal water vapor convergence into the model domain computed from the ERA-40 reanalysis is 47% higher than that from the NCEP-R2 reanalysis. These biases may be reduced by using an ensemble average of NCEP-R2 and ERA-40 as lateral boundary forcing. The multi-year simulation forced by NCEP-R2, ERA-40, JRA-25, and their ensemble mean confirms this conclusion. An optimal ensemble method-- Bayesian model averaging - is later used for the 1998 case. Four reanalyses, their ensemble mean, and their BMA ensemble mean were used as the lateral boundary forcing in the latter case. We used satellite water–vapor-path data as observed truth-and-training data to determine the posterior probability (weight) for each forcing dataset, using the BMA method. The experiment forced by the equal-weight ensemble reduced the circulation biases significantly but reduced the precipitation biases only moderately. However, the experiment forced by the BMA ensemble outperformed not only the experiments forced by individual reanalysis datasets, but also the equal-weight ensemble experiment in simulating the seasonal mean circulation and precipitation. These results suggest that using ensemble forcing is an effective method for reducing the uncertainties in lateral-boundary forcing and improving model performance in regional climate downscaling.

▶ Presenter: Ms. Hyun-Ju Lee of the Climate Prediction Team

▶ Title: Evaluation of the retrospective seasonal prediction skill of individual climate models in the APCC seasonal forecast system

▶ Abstract:

The Asia Pacific Economic Cooperation Climate Center (APCC) has collected dynamic ensemble seasonal prediction data from sixteen operational research institutions in nine APEC member economics to use as inputs for the APCC Climate Prediction System since 2007. The Center produces 1-month lead 3-month mean climate forecasts with four deterministic (based on ensemble mean) and one probabilistic (based on ensemble mean and ensemble spread) forecasts and disseminates them to the APEC member economies every month. Recently, several individual climate models of various research institutes have improved their seasonal forecast system based on physical basis. However, lack understanding of how much the individual climate models have improved and the model behavior for seasonal climate prediction. Therefore, it is necessary to quantitatively assess the performance of individual climate models during the hindcast period from1983 to 2003.

In order to perform this assessment, first we will verify the performance of the individual climate models in the APCC system according to the SVSLRF (Standardized Verification System for Long–Range Forecasts) methodology from WMO. The mean Squared Skill Score (MSSS) is related to phase errors (through the correlation), amplitude errors (through the ratio of the forecast to observed variances) and overall bias error, respectively. Further information about the MSSS and ROC (Relative operating characteristic) is detailed at http://www.wmo.int/.

Data used for assessing the performance of the models includes the National Centers for Environmental Prediction (NCEP)-Department of Energy (DOE) reanalysis 2 (Kanamitsu et al., 2002), Climate Prediction Center Merged Analysis of Precipitation (CMAP) (Xie and Arkin, 1997) and NOAA Optimum Interpolation (OI) Sea Surface Temperature (SST) V2 (Reynolds et al. 2002) for the period from 1983-2003.

This can be useful for the assessment of individual climate models and understanding the multi-model ensemble seasonal prediction, as we can better understand the strengths and weaknesses of the individual climate models.

▶ Presenter: Ms. Hyojin Lee of the Climate Analysis Team

▶ Title: Application of Kernel method to Statistical Downscaling (case study for South Korea)

▶ Abstract:

Downscaling methods are used for obtaining finer grid-scale data from large-scale data. Statistical downscaling methods can reduce the difference between the hindcast and the oberserved data by capturing the highest correlated regions among station data. However, this method requires Global Climate Models (GCM) for capturing data, and it still suffers over-fitting and fishing problems.

Statistical downscaling using the kernel method directly computes the mean from large-scale data near the target area, which can solve existing statistical downscaling problems.

This study compared downscaling methods with two different approaches. The first approach was based on statistical downscaling and the second was based on dynamical downscaling. The statistical downscaling method used canonical correlation analysis and simple linear regression and was developed by the Asia-Pacific Economic Cooperation Climate Center (APCC) team. The second approach used the Weather Research and Forecasting (WRF) model for dynamical downscaling. These downscaling models were initialized by APCC CCSM3.

In this case study, the predictand is temperature that was observed in regions of Busan and Seoul, South Korea. The steps for applying the kernel method were firstl comparing the correlation coefficient to see the similarities between the station data and then running the Friedman test to see the difference between the station data and the results of the three models. Finally, the Wilcoxon test was applied to see the specific differences between the station data and each model.

We believe that the kernel method developed in this project is more powerful than other downscaling methods and requires less computing time.

▶ Presenter: Ms. Hyun-Young Jo of the Climate Analysis Team

▶ Title: Bias correction and Downscaling of CMIP5 model using CA

▶ Abstract:

We investigate the future changes in East Asia using the Constructed Analogues (CA) method.

The CA statistical forecast method is based on the premise that an analogue for a given coarse-scale daily weather (target) pattern can be constructed by combining the weather patterns for several days (predictors) from a library of previously observed patterns. It can be used to study the impacts of climate changes and climate variability. This study statistically downscales and corrects the bias of daily temperature, maximum and minimum temperature, precipitation and daily surface downwelling shortwave radiation data from the CMIP5 model over East Asia using the CA method.

Based on these downscaled historical (1979-2005), RCP4.5, RCP8.5 (2021-2047) data from nine CGCMs, we analyze the changes in future climate.

To produce reliable results, the raw and statistically downscaled model outputs for the current climate were compared with observations. The results show that the linearly downscaled constructed patterns are similar to observed patterns. In other words, the downscaled results reasonably capturs the temporal and spatial distribution of the current temperature and precipitation associated with topography. This provides reliability in assessments of regional changes over East Asia.

For the future climate, the results for downscaled temperatures and precipitation display an increasing trend over East Asia, especially the most significant increase in RCP8.5.

In order to quantify the future changes, an ensemble of nine CGCMs was compared against current observations, which showed an increase for the entire region. The spatial patterns in future climate predicted by all CGCMs are similar ensembles.

▶ Presenter: Ms. Yeomin Jeong of the Climate Analysis Team

▶ Title: Comparison of dynamical and statistical downscaling for the dry season over Southeast Asia

▶ Abstract:

A downscaling approach is a widely used method to obtain high resolution data from coarse GCM/reanalysis resources (~100 km), through both statistical and dynamical methods. This downscaling is important because spatially coarse datasets often misrepresent features of important meteorological variables. Our attention is primarily directed at surface climate variables: precipitation and surface air temperature produced by dynamical and statistical downscaling methods during the dry season over Southeast Asia. These two are the most basic variables to be predicted at seasonal time scales to generate a seasonal outlook.

The statistical downscaling method developed by the Asia-Pacific Economic Cooperation Climate Center (APCC) team uses canonical correlation analysis and simple linear regression. This approach requires long-term data measurements. Monthly high resolution grid (0.5°) precipitation and temperature data (CRU TS3.10) for the period from 1901-2009 were used for the observed datasets (predictand). The CRU data does not include ocean area and contains 3294 grid points on lands for the selected research domain (lon: 80~130, lat: -12 ~30.5). Datasets simulated by APCC/CCSM3 for Jun, Jul, Aug, and JJA during the 27-year period from 1983-2009 were considered. For dynamical downscaling, the Weather Research and Forecasting (WRF, version 3.4), was used with a 15-km horizontal resolution, nested in a larger 45km horizontal resolution focusing on Indonesia in Southeast Asia. This simulation was performed with APCC/CCSM3 data as initial and boundary conditions for comparison with the statistical downscaling method. Evaluation of forecast skill added by both the dynamical and statistical downscaling methods will be investigated.

* About the International Meeting on Statistical Climatology (IMSC)?

The IMSC (http://cccma.seos.uvic.ca/imsc/) has been organized by a group of independent climatologists and statisticians since 1979. The aim of IMSC is to promote good statistical practice in the atmospheric and climate sciences and to maintain and enhance the lines of communication between the atmospheric and statistical science communities.