CPC) Merged Analysis Of Precipitation Research Articles

SM2RAIN-ASCAT satellite-based spatiotemporal drought monitoring using multiscale WT-VMD-ENERGY method

Drought is one of the severe natural disasters that has devastating effects on various parts of the environment. Therefore, spatiotemporal monitoring of drought using reliable methods is very important. In this study, using a new Discrete Wavelet Transform (DWT)-Variational Mode Decomposition (VMD)-Energy multi-scale approach, the efficiency of the new Soil Moisture to Rain-Advanced SCATterometer (SM2RAIN-ASCAT) precipitation product was investigated in monitoring the spatiotemporal patterns of short- to long-term droughts for the Northwest part of Iran. In this regard, after validating the accuracy of the SM2RAIN-ASCAT datasets, a multi-scale method was developed based on the serial decomposition of the drought signals. The mean energy amounts of the obtained subseries were imposed into the K-means (K signifies the number of clusters) to identify the drought-prone regions. Results showed that the highest Correlation Coefficient (R) between SM2RAIN-ASCAT and in-situ observations are found at monthly time scales, in which 73% of the stations had R higher than 0.7. Investigating the reliability of this product for drought monitoring showed that for short- and mid-term timescales in 75% of the stations, the values of R were higher than 0.7. The applied methodology successfully showed the drought-prone regions and a direct relationship was found between energy and drought intensity. The capability of the applied methodology was verified via the CPC Merged Analysis of Precipitation (CMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) products and the obtained results proved the desirable performance of the SM2RAIN-ASCAT datasets. It was found that SOI, Nino3, and Nino3.4 had major effects on the drought index in the selected area, while AO had the least effect. The relationship between the LST and SPIs was negative, while there was a positive relationship between NDVI and SPIs.

Does Antarctic Oscillation have links and influence precipitation over the Uruguay River Drainage Basin in Southeastern South America?

AbstractThis article examines the links and the influence of Antarctic Oscillation (AAO) on precipitation over the Uruguay River Drainage Basin and adjacent regions, situated in Southeastern South America. In this article, we used monthly data of AAO Index obtained from the Climate Prediction Center/National Centers for Environmental Prediction (CPC/NCEP); monthly data of 500 hPa vertical motion through omega variable (ω = Dp/Dt) and converted to vertical velocity (cm/s) from the NCEP/NCAR Reanalysis; monthly data of precipitation from the NCEP Reanalysis 2 and monthly data of precipitation rate from the CPC Merged Analysis of Precipitation (CMAP) with a latitude × longitude spatial resolution of 2.5° × 2.5°. All data are monthly means and were obtained from January 1979 to December 2008. The methodological procedures used Grid Analysis and Display System (GrADS) software to generate composites of vertical motion and precipitation rates. In the case of CMAP precipitation data, the methodology consists of applying the Aspin Welch statistical test, which verifies the statistical significance of the difference between two means using a significance level of 5%. The study region indicates a tendency to present higher (lower) mean rates of precipitation during the AAO negative (positive) phase. The vertical motion analysis results corroborate with the precipitation rates since the higher vertical motions were found during the negative phase of AAO. The statistical test showed, to some areas of Uruguay River Drainage Basin and adjacent regions, statistically significant differences between mean rates of precipitation observed in both phases of AAO.

Change in rainfall per-decades over Java Island, Indonesia

The frequency of hydro-meteorological disasters (climate and weather-related) in Indonesia has continued to increase in the last ten years. Java Island is the region with the highest disaster occurrence in Indonesia. The CMAP (the CPC Merged Analysis of Precipitation) rainfall data for the long time period 1981-2016 was used in this study. While the in situ data was used for verification of CMAP data. The rainfall is one of the main parameters that affects the climate in Indonesia. A study using this long-term data has been done to investigate the changes of rainfall occurring in Java Island per-decade. The results showed that the annual rainfall pattern appears to be almost the same during the all periods (period I, II, III and IV), but the intensity of the annual rainfall changes significantly. While the real changes are also seen in seasonal rainfall patterns and their intensity on Java Island. These changes indicate that the average rainfall in period IV is higher than period I, II and III. Likewise, the early of the dry/rainy season and the length of the season appear to be shifting in six locations from seven locations of case studies conducted. The CMAP data is quite well as alternative data in climate-related research because it has a good verification of in situ data.

Seasonal Variations of Aerosol Optical Depth over East China and India in Relationship to the Asian Monsoon Circulation

Seasonal variation features of aerosol optical depth (AOD) over East China and India in association with the Asian monsoon system are investigated, based on the latest AOD data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, the NCEP Final (FNL) Operational Global Analysis data, the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) data, and the NCEP/NCAR reanalysis data from March 2000 to February 2017. The results indicate that AOD in East China is significantly larger than that in India, especially in spring. The seasonal mean AOD in East China is high in both spring and summer but low in fall and winter. However, the AOD averaged over India is highest in summer and lower in spring, fall, and winter. Analysis reveals that AOD is more closely related to changes in surface wind speed in East China, while no obvious relation is found between precipitation and the AOD distribution on the seasonal timescale. As aerosols are mainly distributed in the atmospheric boundary layer (ABL), the stability of the ABL represented by Richardson number (Ri) is closely correlated with spatial distribution of AOD. The upper and lower tropospheric circulation patterns significantly differ between East China and India, resulting in different effects on the AOD. The effect of advection associated with lower tropospheric circulation on the AOD and the influence of convergence and divergence on the AOD distribution play different roles in maintaining the AOD in East China and India. These results improve our understanding of the mechanism responsible for and differences among the aerosol changes in East China and India.

Regime shift of global oceanic evaporation in the late 1990s using OAFlux dataset

After the decadal change being marked by a distinct transition from a downward trend to an upward trend around 1977–1978, the global oceanic evaporation is found to present a regime shift to downward trend from 2000 onwards by using the Objectively Analyzed air-sea Fluxes (OAFlux) dataset. The robustness of post-2000 decreasing trend of global oceanic evaporation featured by OAFlux is fairly confirmed by checking the total precipitation trend from the Global Precipitation Climatology Project (GPCP) and the CPC Merged Analysis of Precipitation (CMAP) datasets via budget constraint. Analysis on the 1999/2000 trend reversal in global mean temporal evolution and local linear trend patterns of evaporation and related variables is performed. Results show that the positive trend of evaporation before 2000 is primarily associated with both the SST warming and the strengthening of near-surface wind, while the negative trend of evaporation after 2000 may be highly correlated with the weakening of near-surface wind speed and reduction in sea-air humidity difference. The post-2000 decreasing of oceanic evaporation mainly effects precipitation trend over oceans via budget constraint, while land precipitation shows no significant decreasing trend, which may be explained by the increasing of evapotranspiration over land.

A Comparative Analysis of the Impacts of Two Types of El Niño on the Central and Eastern Pacific ITCZ

The precipitation data from the Global Precipitation Climatology Project (GPCP) and CPC Merged Analysis of Precipitation (CMAP) were used to investigate the discrepancy of Centre and Eastern Pacific ITCZ (CEP-ITCZ) during two types of El Niño years. Two models of the heat source distribution during two types of El Niño events were constructed, and the causes of different CEP-ITCZ anomalies for two types of El Niño events were analyzed through the Gill model. The results show that the CEP-ITCZ precipitation is approximately 4.0° southward, and the intensity is enhanced by 3.6 mm/day during the mature period of Eastern Pacific El Niño (EP-El Niño), while during the mature period of Central Pacific El Niño (CP-El Niño), it is only 0.8° southward, and the intensity is enhanced by 3.2 mm/day. The meridional mode of the SST anomaly by means of EOF (Empirical Orthogonal Function) can indirectly affect the CEP-ITCZ by influencing the atmospheric Rossby wave response. In CP-El Niño years, the meridional mode of the SST anomaly is weak, and the atmospheric Rossby wave response enhances the northern and southern trade-wind zones at the same time. The anomaly of cross-equatorial flow is weak and the CEP-ITCZ moves southward a little. At the same time, the wind convergence zone is enhanced, and it is more conducive to the vertical transport of water vapor. In EP-El Niño years, the meridional mode of the SST anomaly is strong, and the atmospheric Rossby wave response strengthens the meridional wind on the northern side of the equator, leading to the southward shift of the CEP-ITCZ. At the same time, the wind convergence zone is weakened and widened, and to a certain extent, it suppresses the vertical transport increase of water vapor caused by the sea surface evaporation.

A fusion-based methodology for meteorological drought estimation using remote sensing data

An effective planning and management to deal with potential impacts of drought requires accurate estimation and analysis of this natural complex phenomenon. Application of new fusion approaches using high-resolution satellite-based products, unlike ground-based observations, can provide accurate drought analysis. This study examines three advanced fusion-based methodologies including Ordered Weighted Averaged (OWA) approach based on ORNESS weighting method (ORNESS-OWA) and ORLIKE weighting method (ORLIKE-OWA) as well as K-nearest neighbors algorithm (KNN) to fuse estimations by five individual estimator models using different remotely sensed data products. The precipitation data from Global Precipitation Climatology Project (GPCP), CPC Merged Analysis of Precipitation (CMAP), CICS High-Resolution Optimal Interpolation Microwave Precipitation from Satellites (CHOMPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM), The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) and Global Land Data Assimilation System Version-2 (GLDAS-2) products is utilized in estimating nonparametric-SPI as a meteorological drought index versus ground-based observations analysis. To achieve more accurate drought estimation, ground-based observations are classified in different clusters based on K-means clustering algorithm. Five individual Artificial Intelligence (AI) models including Multi-Layer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS), M5P model tree, Group Method of Data Handling (GMDH) and Support Vector Regression (SVR) are developed for each cluster and their best results are used in fusion process. In addition, the Genetic Algorithm (GA) optimization model is utilized to determine optimal weights in weighting methods. Estimation performance of all models are evaluated using statistical error indices of Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and coefficient of determination (R2). Application of proposed methodology is verified over Fars province in Iran and the results are compared. Results showed that ORNESS-OWA method with lowest estimation error (MARE of 2.51% and R2 of 95%) had the superb performance in comparison with all other individual AI and fusion-based models. Also, the proposed framework based on remotely sensed precipitation data and fusion-based models demonstrated an effective proficiency in drought estimation.

Anomalous Atmospheric Circulation Associated with Recent West African Monsoon Rainfall Variability

The recent West African Monsoon (WAM) wet season (May to October) rainfall’s interannual variability has been examined with emphasis on the rainfall zones of Guinea Coast (GC), Western Sudano Sahel (WSS) and Eastern Sudano Sahel (ESS) in wet and dry years. Rainfall observations from Climate Research Unit (CRU) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), and atmospheric circulation fields from National Center for Environmental Prediction (NCEP) were evaluated from 1979 to 2014. The objectives are to evaluate the trends across the zones and their linear relationship with the identified oceanic pulsations, as well as examine the evolution of the anomalous atmospheric circulation associated with the wet and dry years during the wet season months. The results show remarkable variability across the rainfall zones. The areal averaged rainfall anomalies show significant correlation values of -0.2 with Ocean Nino Index (ONI) only on WSS and ESS respectively, whereas with South Atlantic Ocean Dipole Index (SAODI) it shows significant correlation value of 0.3 only on GC, at 95% Confidence Level from a t-test. The analysis of trends in spatial and temporal patterns of the atmospheric circulation fields has extensively presented attributes associated with the wet seasonal rainfall anomalies in the wet and dry years. FGOALS-s2 model showed an outstanding simulation of the spatial and temporal patterns of these attributes, with the discrepancies noted, hence presenting itself as a viable tool in the prediction of seasonal rainfall extremes over West Africa.

The East Asian subtropical summer monsoon: Recent progress

The East Asian subtropical summer monsoon (EASSM) is one component of the East Asian summer monsoon system, and its evolution determines the weather and climate over East China. In the present paper, we firstly demonstrate the formation and advancement of the EASSM rainbelt and its associated circulation and precipitation patterns through reviewing recent studies and our own analysis based on JRA-55 (Japanese 55-yr Reanalysis) data and CMAP (CPC Merged Analysis of Precipitation), GPCP (Global Precipitation Climatology Project), and TRMM (Tropical Rainfall Measuring Mission) precipitation data. The results show that the rainy season of the EASSM starts over the region to the south of the Yangtze River in early April, with the establishment of strong southerly wind in situ. The EASSM rainfall, which is composed of dominant convective and minor stratiform precipitation, is always accompanied by a frontal system and separated from the tropical summer monsoon system. It moves northward following the onset of the South China Sea summer monsoon. Moreover, the role of the land–sea thermal contrast in the formation and maintenance of the EASSM is illustrated, including in particular the effect of the seasonal transition of the zonal land–sea thermal contrast and the influences from the Tibetan Plateau and midlatitudes. In addition, we reveal a possible reason for the subtropical climate difference between East Asia and East America. Finally, the multi-scale variability of the EASSM and its influential factors are summarized to uncover possible reasons for the intraseasonal, interannual, and interdecadal variability of the EASSM and their importance in climate prediction.

Decadal trends of the annual amplitude of global precipitation

AbstractIn this study, decadal trends of the annual amplitude of global precipitation are compared in Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and National Centers for Environmental Prediction (NCEP) reanalysis data sets. The analysis reveals decreasing trends in the CMAP and reanalysis data and a flat trend in the GPCP data. The decreasing trends are mainly associated with the increasing trend of low annual minimum precipitation rate in the CMAP data and high annual minimum precipitation rate in the reanalysis data. The trend in the GPCP data is flat because of the balance between decreasing trends along equatorial oceans and increasing trends over subtropical oceans.

Simulation of present-day precipitation over India using a regional climate model

The objective of the present paper is to examine the capability of the regional climate model version 3 (RegCM3) to simulate the annual as well as seasonal precipitation variability over the Indian subcontinent. RegCM3 has been run at 40 km horizontal resolution for the period of 1982–2006 continuously and model results were compared to the observed precipitation datasets of India Meteorological Department (IMD) and CPC Merged Analysis of Precipitation (CMAP). Model evaluation has been done using different statistical methods like mean bias error (MBE), root mean square error, mean percentage error (MPE) and studied the spatial pattern of annual and seasonal variability and trend. Daily precipitation data at 1° × 1° grids of IMD have been used to study observed climatological means (both annual and seasonal), regression trends, interannual and intraseasonal variability over India from 1951 to 2007. The spatial distribution of annual precipitation shows a decreasing trend over west coast of India, central India, hilly region of India and an increasing trend is found over the northwest India, peninsular India and northeast India. The temporal distribution of daily precipitation shows highest rainfall of 18 mm/day in mid July (in composite flood cases only) and 12 mm/day during August (in composite drought cases only). The RegCM3 simulated annual and seasonal precipitation variability is close to the observed IMD and CMAP over all India (AI). During winter and pre-monsoon season, the model has overestimated the mean precipitation while underestimated in summer and post-monsoon season. Overall, annual precipitation showed the deficiency of −22.44 % compared to IMD and −1.41 % compared to CMAP over India. To understand the possible cause of annual and seasonal precipitation biases over India and its six homogeneous regions, the vertical difference (model mines National Centre for Environmental Prediction; NCEP) fields of water vapor mixing ratio (WVMR) and air temperature from surface to top level (1σ–18σ level) have been studied in detail. The model produced more moisture at the lower level during winter (JF) and pre-monsoon (MAM) season and reduced WVMR at the same levels during summer (JJAS) and post (OND) monsoon over India. High moisture content at the lower level simulated in the model can explain the increased precipitation during JF and MAM season and vice versa during JJAS and OND. Similar results were found over the homogeneous regions of India. This analysis indicates that the RegCM3 has more transport of vertical WVMR from surface to mid layer of the troposphere compared to NCEP.

Seasonal variation and physical properties of the cloud system over southeastern China derived from CloudSat products

Based on the National Centers for Environmental Prediction (NCEP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) data and CloudSat products, the seasonal variations of the cloud properties, vertical occurrence frequency, and ice water content of clouds over southeastern China were investigated in this study. In the CloudSat data, a significant alternation in high or low cloud patterns was observed from winter to summer over southeastern China. It was found that the East Asian Summer Monsoon (EASM) circulation and its transport of moisture leads to a conditional instability, which benefits the local upward motion in summer, and thereby results in an increased amount of high cloud. The deep convective cloud centers were found to coincide well with the northward march of the EASM, while cirrus lagged slightly behind the convection center and coincided well with the outflow and meridional wind divergence of the EASM. Analysis of the radiative heating rates revealed that both the plentiful summer moisture and higher clouds are effective in destabilizing the atmosphere. Moreover, clouds heat the mid-troposphere and the cloud radiative heating is balanced by adiabatic cooling through upward motion, which causes meridional wind by the Sverdrup balance. The cloud heating-forced circulation was observed to coincide well with the EASM circulation, serving as a positive effect on EASM circulation.

Decadal trends of global precipitation in the recent 30 years

AbstractDecadal trends of global precipitation are examined using the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Global Precipitation Climatology Project (GPCP), and National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data. The decadal trends of global precipitation average diverge a decreasing trend for the CMAP data, a flat trend for the GPCP data, and an increasing trend for the reanalysis data. The decreasing trend for the CMAP data is associated with the reduction in high precipitation. The flat trend for the GPCP data is related to the offset between the increase in high precipitation and the decrease in low precipitation. The increasing trend for the reanalysis data corresponds to the increase in high precipitation.

Predictive reliability of summer precipitation in China based on error distribution of numerical model

In this paper, we made some statistical analyses on precipitation prediction errors of coupled global climate model of National Climate Center based on CPC merged analysis of precipitation. Through the statistics of the forecast results of summer precipitation in many years and the errors, we find that the distribution of the model prediction errors satisfies the Gaussian distribution. Based on the Gaussian distribution characteristics, the ability to predict the mode for summer precipitation in China can be analyzed and compared. The forecast performance of the dynamic-statistics scheme is significantly better than that of system error correction scheme of the model. The dynamic-statistics optimal combination of factor revise is corrected, relative to error distribution pattern of the system revised forecast, and there are obtained two kinds of error distribution characteristics: 1) improvement of the amplitude; 2) improvement of the displacement. Based on the statistical characteristics of the prediction error, measure of the credibility of model forecast results is proposed to quantitatively assess the credibility of the numerical model forecast results in different regions. Taking the summer precipitations in 2012 and 2013 for example, we analyze the credibility of actual observations, prediction and drought or flood level. Comparing actual observations and prediction, we find that there is a good consistency where the credibility of them is larger, while there are many bad predictions in the region where the credibility of them is smaller. What is more, compared with other reliability test method, the credibility of the abnormal precipitation forecast by this method is more accurate, showing the effectiveness of the method.

African Rainfall Climatology Version 2 for Famine Early Warning Systems

AbstractThis paper describes a new gridded, daily 29-yr precipitation estimation dataset centered over Africa at 0.1° spatial resolution. Called the African Rainfall Climatology, version 2 (ARC2), it is a revision of the first version of the ARC. Consistent with the operational Rainfall Estimation, version 2, algorithm (RFE2), ARC2 uses inputs from two sources: 1) 3-hourly geostationary infrared (IR) data centered over Africa from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and 2) quality-controlled Global Telecommunication System (GTS) gauge observations reporting 24-h rainfall accumulations over Africa. The main difference with ARC1 resides in the recalibration of all Meteosat First Generation (MFG) IR data (1983–2005). Results show that ARC2 is a major improvement over ARC1. It is consistent with other long-term datasets, such as the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), with correlation coefficients of 0.86 over a 27-yr period. However, a marginal summer dry bias that occurs over West and East Africa is examined. Daily validation with independent gauge data shows RMSEs of 11.3, 13.4, and 14, respectively, for ARC2, Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis 3B42, version 6 (3B42v6), and the CPC morphing technique (CMORPH) for the West African summer season. The ARC2 RMSE is slightly higher for Ethiopia than those of CMORPH and 3B42v6. Both daily and monthly validations suggested that ARC2 underestimations may be attributed to the unavailability of daily GTS gauge reports in real time, and deficiencies in the satellite estimate associated with precipitation processes over coastal and orographic areas. However, ARC2 is expected to provide users with real-time monitoring of the daily evolution of precipitation, which is instrumental in improved decision making in famine early warning systems.

High-Resolution Monthly Rainfall Database for Ethiopia: Homogenization, Reconstruction, and Gridding

Abstract Recent heightened concern regarding possible consequences of anthropogenically induced global warming has spurred analyses of data aimed at detection of climate change and more thorough characterization of the natural climate variability. However, there is greater concern regarding the extent and especially quality of the historical climate data. In this paper, rainfall records of 233 gauge stations over Ethiopia for the 1978–2007 period are employed in an analysis that involves homogenization, reconstruction, and gridding onto a regular 0.5° × 0.5° resolution grid. Inhomogeneity is detected and adjusted based on quantile matching. The regularized expectation-maximization and multichannel singular spectrum analysis algorithms are then utilized for imputation of missing values, and the latter has been determined to have a marginal advantage. Ordinary kriging is used to create a gridded monthly rainfall dataset. The spatial and temporal coherence of this dataset are assessed using harmonic analysis, self-organizing maps, and intercomparison with global datasets. The self-organizing map delineates Ethiopia into nine homogeneous rainfall regimes, which is consistent with seasonal and interannual rainfall variations. The harmonic analysis of the dataset reveals that the annual mode accounts for 55%–85% of the seasonal rainfall variability over western Ethiopia while the semiannual mode accounts for up to 40% over southern Ethiopia. The dataset is also intercompared with Global Precipitation Climatology Project (GPCP), Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP), Climatic Research Unit time series version 3 (CRUTS3.0), Tropical Rainfall Measuring Mission (TRMM), and interim ECMWF Re-Analysis (ERA-Interim) rainfall. The correlation of the dataset with global datasets ranges from 0.52 to 0.95 over sparse to dense rain gauge regions. The GPCP rainfall has a small bias and good correlation with the new dataset whereas TRMM and ERA-Interim have relatively large dry and wet biases, respectively.

Recent climate change in the Arabian Peninsula: Seasonal rainfall and temperature climatology of Saudi Arabia for 1979–2009

Attempts are made to study the seasonal climatology of the Arabian Peninsula, including the regional to station level information for Saudi Arabia for the period 1979–2009. The wet (November to April) and dry (June to September) season rainfall and temperature climatology are obtained from various data sources, namely, surface observations, CPC Merged Analysis of Precipitation (CMAP), Climatic Research Unit (CRU) and Tropical Rainfall Measuring Mission (TRMM). These gridded datasets detect the dry zone over the Rub Al-Khali, the world's largest sand desert, during the wet season. In this season, large rain belts exist north of 30°N and south of 15°N. During the dry season, the Arabian Peninsula is almost entirely dry north of 15°N but rain belts exist below this latitudinal boundary. Irrespective of the season or dataset used, a relatively heavy-rain area is obtained for the southwest of the Peninsula. The wet (dry) season temperature is highest over the western (middle to the northern) parts of the Peninsula.Surface observations indicate that, irrespective of season, rainfall insignificantly increased in the first period (1979–1993), and then significantly decreased in the second period (1994–2009). The decrease rate is 35.1mm (5.5mm) per decade during the wet (dry) season. The temperature over Saudi Arabia has increased significantly, and the increase rate is faster (0.72°C per decade) in the dry season compared to the wet season (0.51°C per decade).

Opposite trends in summer precipitation in South and North Korea

AbstractWe analysed daily precipitation data at the rain gauge stations in North Korea over a period of 25 years from 1983 to 2007, and in South Korea over a period of 35 years from 1973 and 2007. We found a striking trend of decreasing summer precipitation across North Korea. By contrast, in South Korea, the trend is opposite: there is a major increase in summer precipitation. Also, the number of dry days in summer showed an increasing trend in North Korea and a decreasing trend in South Korea. For the number of days with heavy precipitation (i.e. days with above 50 mm/day daily precipitation) during summer, a decreasing trend was detected in North Korea, but no trend in South Korea. However, in South Korea, there was a significant increase of days with heavy precipitation over the whole year. These opposite trends in summer precipitation between North and South Korea were further confirmed using four global/regional satellite and rain gauge datasets of CPC Merged Analysis of Precipitation (CMAP), the Global Precipitation Climatology Project (GPCP), Precipitation REConstruction over the Land (PREC/L), and the Asian Precipitation‐Highly Resolved Observation Data Integration Towards the Evaluation of Water Resources (APHRODITE). Copyright © 2011 Royal Meteorological Society

A dynamical fingerprint of tropical Pacific sea surface temperatures on the decadal-scale variability of cool-season Arctic precipitation

[1] The temporal and spatial characteristics of decadal-scale variability in the Northern Hemisphere (NH) cool-season (October–March) Arctic precipitation are identified from both the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP) precipitation data sets. This decadal variability is shown to be partly connected to the decadal-scale variations in tropical central Pacific sea surface temperatures (SSTs) that are primarily associated with a decadal modulation of the El Niño–Southern Oscillation (ENSO), i.e., transitions between periods favoring typical eastern Pacific warming (EPW) events and periods favoring central Pacific warming (CPW) events. Regression and composite analyses reveal that increases of central Pacific SSTs drive a stationary Rossby wave train that destructively interferes with the wave number-1 component of the extratropical planetary wave. This destructive interference is opposite to the mean effect of typical EPW on the extratropical planetary wave. It leads to suppressed upward propagation of wave energy into the polar stratosphere, a stronger stratospheric polar vortex, and a tendency toward a positive phase of the Arctic Oscillation (AO). The positive AO tendency is synchronized on the decadal scale with a poleward shift of the NH storm tracks, particularly in the North Atlantic. Storm track variations further induce changes in the amount of moisture transported into the Arctic by synoptic eddies. The fluctuations in the eddy moisture transport ultimately contribute to the observed decadal-scale variations in the total Arctic precipitation in the NH cool season.

Evaluation of the DEMETER performance for seasonal hindcasts of the Indian summer monsoon rainfall

On the basis of the observed all-India rainfall (AIR), Climate Prediction Center (CPC) merged analysis of precipitation (CMAP), and the multimodel rainfall simulated in the Development of a European multimodel ensemble system for seasonal to interannual prediction (DEMETER) project, the model performance in terms of forecast quality of the seasonal mean Indian summer monsoon rainfall (ISMR) was evaluated. The approach used included the individual model (single-model ensemble) comparison as well as its comparison with the multimodel ensemble (MME). The results obtained reveal that systematic biases are the main source of the low skill in predicting the ISMR, while improved reproduction of interannual variability may increase the overall forecast quality for the ISMR. The MME shows superior performance in reproducing AIR features than the single-model ensembles. This improved forecast quality achieved by the MME with respect to the ISMR variations primarily depends on model performance and secondly on ensemble size. The MME has improved capability of simulating the ISMR variations during 1972–1990 decades, whereas it exhibits a lower performance in the 1990s. The reason of this decadal varying forecast quality clearly deserves more investigation. In summary, these results suggest that the MME's increase in skill absolutely depends on enhancing the individual model quality. Copyright © 2011 Royal Meteorological Society