EVALUASI PERFORMA INDEKS MONSUN AUSMI DAN WNPMI DI WILAYAH INDONESIA

Intisari Iklim di wilayah Indonesia sangat dipengaruhi oleh aktivitas monsun Asia-Australia. Variabilitas kedua sistem monsun tersebut dapat direpresentasikan dengan baik masing-masing oleh indeks monsun Australian Summer Monsoon Index (AUSMI) dan Western North Pacific Monsoon Index (WNPMI). Saat ini, BMKG secara operasional menggunakan indeks AUSMI dan WNPMI untuk memonitor aktivitas monsun di wilayah Indonesia sebagai bahan prakiraan musim. Meskipun banyak literatur menyatakan bahwa wilayah Indonesia merupakan bagian dari sistem monsun Asia-Australia, namun kondisi topografi lokal yang kompleks berpotensi memodifikasi sirkulasi monsun sehingga perlu dikaji performa kedua indeks tersebut sebelum digunakan secara operasional. Penelitian ini dilakukan untuk menguji performa indeks monsun AUSMI dan WNPMI dalam menggambarkan variasi antartahunan (interannual), variasi dalam musim (intraseasonal), dan siklus tahunan (annual cycle) hujan monsun Indonesia. Hasil penelitian mengungkapkan bahwa kedua indeks memiliki performa yang sangat baik hanya di wilayah dimana indeks tersebut didefinisikan namun kurang baik untuk wilayah Indonesia seperti yang ditunjukan oleh nilai koefisien korelasi yang tidak signifikan dari hasil uji statistik antara kedua indeks dengan curah hujan dari Global Precipitation Climatology Project (GPCP) pada periode 1981-2010. Selain itu, kedua indeks juga memperlihatkan karakteristik siklus tahunan yang berbeda dengan karakteristik siklus tahunan hujan wilayah Jawa sebagai wilayah kunci monsun Indonesia. Hasil ini mengindikasikan perlunya pendefinisian indeks sendiri untuk memonitor aktivitas monsun di wilayah Indonesia. Abstract The climate of Indonesia is strongly affected by the Asian-Australian monsoon system. The variability of the two monsoon systems can be well represented by the Western North Pacific Monsoon Index (WNPMI) and the Australian Summer Monsoon Index (AUSMI) respectively. For producing seasonal forecast, BMKG uses the WNPMI and AUSMI monsoon index to monitor monsoon activity in Indonesia. Although most literature states that the Indonesian region is part of the Asian-Australian monsoon system, the complex local topography may modify the monsoon circulation. Hence, it is necessary to assess the performance of the two indices before they are operationally used. This study was conducted to evaluate the performance of the AUSMI and WNPMI monsoon indices in describing the annual cycle, intraseasonal and interannual variability of the Indonesian monsoon rainfall. The results revealed that the two indices only performed very well in the areas where the index was defined but lack of skill for the Indonesian region because of insignificant linear correlation based on a statistical significance test between the two indices and the Global Precipitation Climatology Project (GPCP) rainfall in the 1981-2010 period. In addition, both monsoon indices and Java rainfall showed different characteristics of the annual cycle. These results indicate that it is necessary to define a specific index for monitoring monsoon activity in Indonesia.

On the Importance of Gauge-Undercatch Correction Factors and Their Impacts on the Global Precipitation Estimates

Precipitation gauges are critical for measuring precipitation rates at regional and global scales and are often used to calibrate precipitation rates estimated from other instruments such as satellites. However, precipitation measured at the gauges is affected by gauge-undercatch that is often larger for solid precipitation. In the present work, two popular gauge-undercatch correction factors are assessed: one utilizes a dynamic correction model and is used in the Global Precipitation Climatology Centre (GPCC) Monitoring product and the other one employs a fixed climatology and is used in the Global Precipitation Climatology Project (GPCP) product. How much the choice of correction factors can impact the total estimate of precipitation was quantified over land at seasonal, annual, regional, and global scales. The correction factors are also compared as a function of the environmental variables used in their development, among those are near-surface air temperature, relative humidity, wind speed, elevation, and precipitation intensity. Results show that correction factors can increase the annual precipitation rate based on the gauges by ~9.5 % over the global land (excluding Antarctica), although this amount can vary from ~6.3% (in boreal summer) to more than 10% (in boreal winter), depending on the season and the method used for gauge-undercatch correction. Annual variations of correction factors can also be large, so the use of the fixed climatology correction factors requires caution. Given their magnitudes and differences, selection of appropriate correction factors can have important implications in refining the water and energy budget calculations.

How Well Does Mpas-atmosphere Simulate the Characteristics of the Botswana High?

The Botswana High is a prominent mid-tropospheric system that modulates rainfall over subtropical southern Africa, but the capability of a Global Climate Model (GCM) to reproduce it remains unknown. This study examines the capability of a GCM with quasi-uniform resolution (Model Prediction Across Scales, hereafter MPAS) in simulating the characteristics of the Botswana High. The MPAS is applied to simulate the global climate at 240km quasi-uniform resolution over the globe for the period 1980-2010. The model results are validated against gridded observation dataset (Climate Research Unit, CRU), satellite dataset (Global Precipitation Climatology Project, GPCP), and reanalysis datasets (Climate Forecast System Reanalysis, CFSR; the National Oceanic and Atmospheric Administration, NOAA; and the European Centre for Medium-Range Weather Forecasts version 5, ERA5). In general, MPAS replicates all the essential features in the climatology of temperature, rainfall, 500 hPa geopotential height and vertical motion over southern Africa, reproduces the spatial and temporal variation of the Botswana High, and captures the influence of the Botswana High on droughts and deep convections over the sub-continent. In addition, the model reproduces well the anomalies in vertical motion over subtropical southern Africa during +ve and -ve phases of the Botswana High. However, the model struggles to reproduce the precipitation pattern associated with the positive and native modes of Botswana high. The results of this study have an application in understanding the characteristics of Botswana High and in improving MPAS for seasonal forecasting over southern Africa.

Observed trends in clouds and precipitation (1983–2009): implications for their cause(s)

Satellite observations (International Satellite Cloud Climatology Project (ISCCP), 1983–2009) of linear trends in cloud cover are compared to those in global precipitation (Global Precipitation Climatology Project (GPCP) pentad V2.2, 1983–2009), to investigate possible cause(s) of the linear trends in both cloud cover and precipitation. The spatial distributions of the linear trends in total cloud cover and precipitation are both characterized primarily by a broadening of the major ascending zone of Hadley circulation. Our correlation studies suggest that global warming, Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO) can explain 67 %, 49 % and 38 %, respectively, of the spatial variabilities in the linear trends in cloud cover, but causality is harder to establish. Further analysis of the broadening of the major ascending zone of Hadley circulation shows that the trend in global temperature, rather than those in AMO and PDO, is the primary contributor to the observed linear trends in total cloud cover and precipitation in 1983–2009. The underlying mechanism driving this broadening is proposed to be the moisture–convection–latent-heat feedback cycle under global warming conditions. The global analysis is extended by investigating connections between clouds and precipitation in China, based on a large number of long-running, high-quality surface weather stations in 1957–2005. This reveals a quantitative matching relationship between the reduction in light precipitation and the reduction in total cloud cover. Furthermore, our study suggests that the reduction in cloud cover in China is primarily driven by global temperature; PDO plays a secondary role, while the contribution from AMO and Niño3.4 is insignificant, consistent with the global analysis.

The Global Precipitation Climatology Project Version 3 Products

<p>The Global Precipitation Climatology Project (GPCP) is currently providing a next-generation Version 3.1 Monthly product, which covers the period 1983-2019.  This modernized product includes higher spatial resolution (0.5°x0.5°); a wider coverage (60°N-S) by geosynchronous IR estimates, now based on the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) algorithm, with monthly recalibration using Goddard Profiling (GPROF) algorithm retrievals from selected passive microwave sensors; and improved calibrations of Television-Infrared Operational Satellite (TIROS) Operational Vertical Sounder (TOVS) and Advanced Infrared Sounder (AIRS) precipitation, used outside 60ºN-S.  The merged satellite estimate is adjusted to the Tropical Combined Climatology (TCC) at lower latitudes, and the Merged CloudSat, TRMM, and GPM (MCTG) climatology at higher latitudes.  Finally, V3.1 provides a merger of the satellite-only estimates with the Global Precipitation Climatology Centre (GPCC) monthly 1°x1° gauge analyses. </p><p>As well, the GPCP team is advancing a companion global Version 3 Daily product, in which the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) Final Run V06 estimates are used where available (initially restricted to 60°N-S), and rescaled TOVS/AIRS data in high-latitude areas, all calibrated to the GPCP V3.1 Monthly estimate.  Since IMERG currently extends back to June 2000, daily PERSIANN-CDR data will be used for the period January 1983–May 2000 to complete the record.</p><p>This presentation will provide early results for, and the latest status of, the Monthly and Daily GPCP products as a function of time and region.  Key points include examining homogeneity over time and across time and space boundaries between input datasets.  One key activity is to refine the V3 products while we continue to produce the Version 2 GPCP products for on-going use.</p>

Evaluating performances of one-year simulation by using 3.5 km mesh global nonhydrostatic model

<p>Recent advancement of supercomputing enables us to conduct a climate simulation by using a global model with horizontal grid spacing of a few kilometers. We may need to tune the model in order to conduct a reliable simulation. In order to test feasibility of a few kilometer climate simulation in near future, we conducted one-year simulation from June 2004 to May 2005 by using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with horizontal grid spacing of 28 km, 14 km, 7 km, and 3.5 km, and evaluated their simulation performances. In general, global models have shown weak wind speed of tropical cyclones compared to its central sea level pressure due to insufficient horizontal resolution. As expected, the 3.5 km simulation showed improvement of this bias. As for simulated mean state, globally annual mean precipitation tended to be decreased with finer horizontal resolution in NICAM. Compared with observation (Global Precipitation Climatology Project V2.2; 2.71 mm day<sup>-1</sup>), 7 km and 3.5 km simulations underestimated the global mean precipitation (2.54 mm day<sup>-1</sup> and 2.67 mm day<sup>-1</sup>), while 14 km and 28 km simulations overestimated (2.84 mm day<sup>-1</sup> and 2.78 mm day<sup>-1</sup>). The 3.5 km simulation showed the best performance for reproducing globally annual mean precipitation. However, the 3.5 simulation showed underestimation of the South Pacific Convergence Zone. In order to conduct a reliable simulation, we need to improve performance of the 3.5 km global model. This demands extensive computing resources. The supercomputer Fugaku will give us extensive computing resources for addressing this issue.</p>

Changes in HIRS Detection of Cloud over Australia from 1985 to 2001

A long-term archive of cloud properties (cloud top pressure, CTP; and cloud effective emissivity, ε) determined from High-resolution Infrared Radiation Sounder (HIRS) data is investigated for evidence of regional cloud cover change. In the 17 years between 1985 and 2001, different cloud types are analysed over the Australian region (10° S–45° S, 105° E–160° E) and areas of change in total cloud frequency examined. Total cloud frequency change over the Australian region between two adjacent eight-year time periods (1994 to 2001 minus 1985 to 1992) shows the largest increases (ranges between 6% and 12%) of average HIRS total cloud cover occurring over the offshore regions to the northwest and northeast of the continent. Over land, the largest reduction of average HIRS total cloud frequency is in the southwestern region of Australia (between 2% and 8%). Through central Australia, there is a 2% to 7% increase in average HIRS total cloud frequency when comparing these eight-year periods. This paper examines the regional cloud changes in 17 years over Australia that are embedded in global cloud statistics. Examining total HIRS cloud cover frequency over Australia and comparing two different eight-year time periods, has highlighted notable areas of average change. Preliminary reporting of satellite-derived HIRS cloud products and Global Precipitation Climatology Project (GPCP) rainfall products during La Niña seasons between 1985 and 2001 has also been undertaken.

Forecast skill of the Indian monsoon and its onset in the ECMWF seasonal forecasting system 5 (SEAS5)

Accurate forecasting of variations in Indian monsoon precipitation and progression on seasonal time scales remains a challenge for prediction centres. We examine prediction skill for the seasonal-mean Indian summer monsoon and its onset in the European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system 5 (SEAS5). We analyse summer hindcasts initialised on 1st of May, with 51 ensemble members, for the 36-year period of 1981–2016. We evaluate the hindcasts against the Global Precipitation Climatology Project (GPCP) precipitation observations and the ECMWF reanalysis 5 (ERA5). The model has significant skill at forecasting dynamical features of the large-scale monsoon and local-scale monsoon onset tercile category one month in advance. SEAS5 shows higher skill for monsoon features calculated using large-scale indices compared to those at smaller scales. Our results also highlight possible model deficiencies in forecasting the all India monsoon rainfall.

GRACE a witness to the Recovery of the Tigris-Euphrates Hydrologic System

<p>Global warming is producing climatic changes across the world that affect in major ways the livelihood of major sectors of the world’s population. Over the past decade or two, an increase in the frequency and intensity of specific climatic phenomena (e.g., hurricanes, wet or dry periods, etc.) has been reported from many parts of the globe and is believed to be climate change-related. Over the past few years, the largest and most intense precipitation events were recorded over the Tigris and Euphrates watershed (TEW), a heavily engineered watershed (> 60 main dams) that is shared by Turkey, Iran, Syria, Saudi Arabia, and Iraq. Analysis of the Global Precipitation Climatology Project (GPCP) precipitation record over the past 40 year (1979-present) across the TEW revealed a prolonged dry period (2002- to 2017; Average Annual Precipitation [AAP]: 240 km<sup>3</sup>), followed by wet years (2018 to 2020; AAP: 425 km<sup>3</sup>). The recent extensive precipitation events during the wet period are reflected in GRACE and GRACE-FO data. Throughout the dry period there was a total decline in GRACE<sub>TWS</sub> of 212 km<sup>3</sup> (13.3 km<sup>3</sup>/yr) followed by an increase of 246 km<sup>3</sup> (82 km<sup>3</sup>/yr) during the wet period.  In other words, in the past 2.5 years, the TEW more than recovered its losses during the previous 15 years. This recovery was enabled in part by the impoundment of surface water behind the many dams in the riparian countries and by infiltration of precipitation that recharged the TEW aquifers. Using radar altimetry we observe an increase in surface water levels by 8 m in Lake Ataturk, 13 m in Lake Karakaya, 1.5 m in Lake Van in Turkey, 5 m in Lake Assad in Syria, and 16 m in Lake Tharthar, and 24 m in Lake Mosul in Iraq.  These translate to a volume increase of 21.7 km<sup>3</sup> in Turkey, 3.5 km<sup>3</sup> in Syria, and 34 km<sup>3</sup> in Iraq during the wet period. Using GRACE data and outputs of land surface models, we estimate that groundwater storage GRACE<sub>TWS</sub> declined at a rate of -7 km<sup>3</sup>/yr during the dry period and increased at a rate of 60 km<sup>3</sup>/yr during the wet years.</p>

Neural network for seasonal climate precipitation prediction on the Brazil

Precipitation is the hardest meteorological field to be predicted. An approach based on and optimal neural network is applied for climate precipitation prediction for the Brazil. A self-configurated multi-layer perceptron neural network (MLP-NN) is used as a predictor tool. The MLP-NN topology is found by solving an optimization problem by the Multi-Particle Collision Algorithm (MPCA). Prediction for Summer and Winter seasons are shown. The neural forecasting is evaluated by using the reanalysis data from the NCEP/NCAR and data from satellite GPCP (Global Precipitation Climatology Project -- monthly precipitation dataset).