Simulation and Evaluation of Statistical Downscaling of Regional Daily Precipitation over North China Based on Self-Organizing Maps

A statistical downscaling method based on Self-Organizing Maps (SOM), of which the SOM Precipitation Statistical Downscaling Method (SOM-SD) is named, has received increasing attention. Herein, its applicability of downscaling daily precipitation over North China is evaluated. Six indices (total season precipitation, daily precipitation intensity, mean number of precipitation days, percentage of rainfall from events beyond the 95th percentile value of overall precipitation, maximum consecutive wet days, and maximum consecutive dry days) are selected, which represent the statistics of daily precipitation with regards to both precipitation amount and frequency, as well as extreme event. The large-scale predictors were extracted from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) daily reanalysis data, while the prediction was the high resolution gridded daily observed precipitation. The results show that the method can establish certain conditional transformation relationships between large-scale atmospheric circulation and local-scale surface precipitation in a relatively simple way. This method exhibited a high skill in reproducing the climatologic statistical properties of the observed precipitation. The simulated daily precipitation probability distribution characteristics can be well matched with the observations. The values of Brier scores are between 0 and 1.5 × 10−4 and the significance scores are between 0.8 and 1 for all stations. The SOM-SD method, which is evaluated with the six selected indicators, shows a strong simulation capability. The deviations of the simulated daily precipitation are as follows: Total season precipitation (−7.4%), daily precipitation intensity (−11.6%), mean number of rainy days (−3.1 days), percentage of rainfall from events beyond the 95th percentile value of overall precipitation (+3.4%), maximum consecutive wet days (−1.1 days), and maximum consecutive dry days (+3.5 days). In addition, the frequency difference of wet-dry nodes is defined in the evaluation. It is confirmed that there was a significant positive correlation between frequency difference and precipitation. The findings of this paper imply that the SOM-SD method has a good ability to simulate the probability distribution of daily precipitation, especially the tail of the probability distribution curve. It is more capable of simulating extreme precipitation fields. Furthermore, it can provide some guidance for future climate projections over North China.

Application of Consecutive Dry Days (CDD) Multi-Model Ensemble (MME) Prediction to Support Agricultural Sector in South Sulawesi Rice Production Centers

Sufficient water availability during the vegetative, reproductive, and early ripening phases of the rice plants is essential. Information on drought, such as Consecutive Dry Days (CDD) predictions in this period, became very crucial and had an important role in maintaining rice production stability. The aim of this study is to investigate the performance of CDD Multi-Model Ensemble prediction, which is applied to South Sulawesi rice production centers. CDD observation was calculated using high resolution gridded precipitation blending data, obtained from BMKG precipitation network stations and the daily-improved Climate Hazards group InfraRed Precipitation with Stations (CHIRPS) version 2.0. The North American Multi-Model Ensemble (NMME) monthly precipitation hindcast data during 1982 – 2010 periods from each nine individual global climate models were used to develop seasonal CDD predictions. World Meteorological Organization (WMO) Standard Verification for Long Range Forecast (SVS-LRF) method applied to describe this CDD prediction performance on four different seasons. Investigation of model performance during strong El Niño event in 1997 also conducted in order to get general skill overview regarding extreme climate event. Best performance of CDD prediction generally occurred during JJA and DJF period. MME CDD prediction shows better performance compared to individual model performance for almost all season. Spatial coherence between prediction and observation over rice production centers during 1997 El Niño confirms the skill of CDD predictions. The application of this prediction on agricultural sector will be very useful in order to support rice production sustainability and food security. Further analysis result can be found on full paper.

High-Resolution North Sulawesi Drought Hazzard Mapping Based on Consecutive Dry Days (CDD)

North Sulawesi is one of the Province in northern Indonesia with high spatial annual rainfall variations and influenced by global climate anomaly that can lead to extreme events and disaster occurrence, such as flood, landslide, drought, etc. The purpose of this study is to generate high-resolution meteorological hazard map based on long-term historical consecutive dry days (CDD) over the North Sulawesi region. CDD was calculated based on observed daily precipitation data from Indonesia Agency for Meteorology, Climatology, and Geophysics (BMKG) surface observation station network (CDDobs) and the daily-improved Climate Hazards group Infrared Precipitation with Stations (CHIRPS) version 2.0 (CDDCHIRPS) during 1981 – 2010 period. The Japanese 55-year Reanalysis (JRA-55) data obtained from iTacs (Interactive Tool for Analysis of the Climate System) with the same time scale period also used to explain physical – dynamical atmospheric properties related to drought hazard over this region. The Geostatistical approach using regression kriging method was applied as spatial interpolation technique to generate high resolution gridded (0.05° × 0.05°) drought hazard map. This method combines a regression of CDDobs as dependent variable (target variable) on CDDCHIRPS as predictors with kriging of the prediction residuals. The results show that most of the areas were categorized as medium drought hazard level with CDD values ranging from 80-100 days. Meanwhile, small islands around main Sulawesi island such as Sangihe and Karakelong island are dominated by low drought hazard levels with CDD values ranging from 50-60 days. The highest levels of drought hazard area are located in South Bolaang Mongondow Regency.

The effects of climate change on the Pleistocene rock art of Sulawesi

The equatorial tropics house some of the earliest rock art yet known, and it is weathering at an alarming rate. Here we present evidence for haloclasty (salt crystallisation) from Pleistocene-aged rock art panels at 11 sites in the Maros-Pangkep limestone karsts of southern Sulawesi. We show how quickly rock art panels have degraded in recent decades, contending that climate-catalysed salt efflorescence is responsible for increasing exfoliation of the limestone cave surfaces that house the ~ 45 to 20-thousand-year-old paintings. These artworks are located in the world’s most atmospherically dynamic region, the Australasian monsoon domain. The rising frequency and severity of El Niño-induced droughts from anthropogenic climate change (that is, higher ambient temperatures and more consecutive dry days), combined with seasonal moisture injected via monsoonal rains retained as standing water in the rice fields and aquaculture ponds of the region, increasingly provide ideal conditions for evaporation and haloclasty, accelerating rock art deterioration.

Twentieth Century Precipitation Trends in the Upper Mzingwane Sub-catchment of the Northern Limpopo Basin, Zimbabwe

This study evaluates precipitation trends in the upper Mzingwane sub-catchment (UMS) of Zimbabwe for the variables of annual precipitation, extremely wet days, consecutive wet days and consecutive dry days. The UMS is of strategic socio-economic significance in terms of its provision of water security and sustenance to livelihoods. The analysis is undertaken at four stations: Bulawayo Goetz, Filabusi, Mbalabala and Matopos National Park, and for the period 1921–2000. In general trends were found not to have local statistical significance, with the exception of the Matopos station (in the westernmost extent of UMS) which records significant increasing (declining) trends for most dryness (wetness) extreme indices. A general north to south-western declining precipitation gradient during the past ~ 69 years over the UMS was also found. The findings provide a baseline for future extended historical and future precipitation trend studies, and are important in the context of the socio-economic impacts of extreme events in this region.

Monthly Variations of the Winter Precipitation over the Philippines During the Mature Phase of Eastern Pacific El Niño

The influence of ENSO events on winter precipitation anomalies in the Philippines has been well known since decades, but whether this effect is different between months needs further exploration. In this study, the monthly variations of precipitation over the Philippines in winter during the mature phases of ENSO events are investigated with datasets of reanalysis and observations from 1979 to 2019. Results indicate that only the eastern Pacific (EP) El Niño shows different influences on the Philippines winter precipitation among different months. In December during mature EP El Niño events, precipitation deficiency is not significant over the whole Philippines, whereas in January and February, precipitation decreases significantly over the southern Philippines as well as the areas to the southeast of the Philippines. Besides, the correlation between consecutive dry days over the southeast Philippines and ENSO is significantly positive in January and February but not in December. The eastward propagation of EP El Niño–related anomalous anticyclone over the western North Pacific (WNPAC) from December to February is proved responsible for the changed relationship between EP El Niño and precipitation. In December, the center of the WNPAC is located to the southeast of the Indo-China Peninsula, inducing weak lower-level wind anomalies and, consequently, weak vertical movement and water vapor transport anomalies over the Philippines, which exerts limited influence on the local precipitation. In January and February, by contrast, the center of WNPAC is located to the southeast of the Philippines, and therefore the southern Philippines is occupied by anticyclonic moisture transports and downward vertical motions, favoring less precipitations and larger than normal consecutive dry days over there.

Detecting climate change in Ukraine: trends, prediction and extreme events

<p>In this study the trends and variations in 25 extreme temperature and precipitation indices<br>defined by ETCCDI, are examined using trend method, probability distribution analysis and<br>spatial statistics for periods of 71 to 137 years for 16 stations evenly distributed in Ukraine. Data<br>on the indices were obtained from www.ecad.eu.<br>Since 1981, temperature has increased by about 1ºC in all stations in question relative to the<br>period of 1945-1980. Analysis of the temperature indices indicates that during the 20th and the<br>beginning of the 21th century there is significant warming which is particularly pronounced in<br>annual mean and annual maximum temperatures. Occurrence of more summer days, warm days<br>and tropical nights and warm spell duration reached the record highest level, and conversely<br>occurrence of frost and ice days, cold days and cold spell duration fall to a record low for the last<br>three decades in the most of study territory.<br>Since 1981, precipitation amount has grown by 30-50 mm relative to the period of 1945-1980 for<br>the most of Ukrainian territory, except Uzhhorod and Uman where precipitation amount has<br>remained the same. For Ukraine average, an increase in maximum daily and maximum 5 days<br>precipitation amount, the maximum number of consecutive wet days, heavy and very heavy<br>precipitation days, and a decrease in the maximum number of consecutive dry days are observed<br>for the last three decades.<br>The analysis of the spatial distribution of trend of precipitation and temperature indices showed<br>that there are large differences between regions of Ukraine, and coherence of spatial distribution<br>of trends of various indices is low.<br>Spectral analysis and harmonic regression techniques were used to derive simulated and<br>predicted (2019-2050) values of annual precipitation and annual mean temperature and four<br>indices such as maximum value of daily maximum temperature, minimum value of daily<br>minimum temperature, the highest 1-day precipitation amount and maximum number of<br>consecutive dry days for some stations such as Kerch (the Crimean Peninsula), Kyiv (situated in<br>north-central Ukraine along the Dnieper River), Lubny (Dnieper Lowland), Lviv and Shepetivka<br>(Podillia Upland), Uzhhorod (Transcarpathia), Uman (Dnieper Upland).<br>Annual mean temperature and maximum value of daily maximum temperature were predicted to<br>increase by 0.33°C per decade in the period of 2019-2050 with respect to 1981-2018, while<br>minimum value of daily minimum temperature was predicted to grow slightly faster (by 0.43-<br>0.63ºC per decade).<br>Precipitation was predicted to increase for the stations in question by 20-66 mm up to 2050<br>relative to 1981-2018 and conversely maximum number of consecutive dry days will slightly<br>decline except Lubny where increase in an aridity index was predicted. In the next three decades<br>changes in maximum daily precipitation will be various: in Shepetivka and Kyiv such<br>precipitation will be decreased and in other stations increasement in such precipitation will be up<br>to 6 mm till 2050 with respect to 1981-2018.</p>

Emergence of multisectoral impacts of the global warming during the 21st century.

<p>The current observed global warming is projected to intensify by the end of the 21<sup>st</sup> century. According to simulations of the climate system and its impacts on populations, previous studies show significant projected impacts on four main sectors: water, health, energy and agriculture. Concurrent analyses have also focused on the time of emergence (ToE) of future climate modifications to assess when new climate regimes will emerge from a prior reference. Here we propose to investigate the timing and the emergence of global warming impacts on populations over three main vulnerable regions: Western Africa (WAF), Eastern Africa (EAF) and South-eastern Asia (SEA). We propose to analyse multi-sectoral impacts that may affect human being by accounting for (but not limited to) 6 fields: crop failure, water scarcity, health, droughts, floods, and heatwaves. The ISIMIP2b protocol (phase 2b of the Intersectoral Impact Model Intercomparison Project), which provides simulated impacts from 1 to 8 sectoral impact models and four CMIP5 (5<sup>th</sup> phase of the Coupled Model Intercomparison Project) climate models, is used in this study.</p><p>              Preliminary results under the RCP8.5 future climate scenario show a strong acceleration of the decrease of the annual maize yields before 2048 in WAF and EAF according to the CLM45 impact model, suggesting a significant emergence at this time. No particular fluctuation from the long-term trend is shown in SEA. CMIP5 climate forcing (i.e. GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) responses in maize yields exhibit larger uncertainties over EAF than over WAF and SEA. Drought metrics such as the annual number of consecutive dry days (i.e. daily precipitations < 1mm) and the annual number of periods with more than 5 consecutive dry days show an acceleration of their increases around 2052 in WAF with large climate forcing uncertainties, but no significant emergence over EAF and SEA. Flood metrics from the ORCHIDEE impact model simulations do not exhibit particular fluctuation nor acceleration of the change during the 21<sup>st</sup> century in the three regions. The next step of our study is to quantify the ToE of the significant fluctuations compared to the long-term trends of the different metrics that cover every impact sectors. The Kolmogorov-Smirnov test (‘KS-test’) method will be applied as the statistical approach to quantify the ToE independently from the signal shape. Impact models uncertainties will also be quantified compared to the climate model uncertainties, in order to assess whether impact or climate modelings is the main driver of the total uncertainties when studying the emergence of the impacts of global warming.</p>