Spatial Patterns Of Precipitation Research Articles

Climate change projections of soil erosion in South America in the XXI century

Global climate projections indicate that continental South America (SA) can experience significant changes in soil erosion caused by water in the coming years due to changes in the precipitation spatial and temporal patterns, threatening food security and biodiversity and impairing agroecosystems to provide services. This study aims to quantify future impacts on soil erosion in the SA provoked by changes in precipitation from climate change scenarios using the Revised Universal Soil Loss Equation (RUSLE). A 20 km spatial resolution of annual precipitation projected from the ensemble of four global climate models (BESM, CanESM2, HadGEM2-ES, and MIROC5 models) was used to assess impacts between 2010 and 2099, sliced every 30 years. The Eta/CPTEC model dynamically regionalized the datasets, and the RCPs 4.5 and 8.5 scenarios were selected to quantify rainfall erosivity and soil erosion rates. The projected average soil erosion over the continent was 37% above the present estimation (3.8 Mg ha−1 year−1), reaching 5.2 Mg ha−1 year−1 at the end of the 21st century. The projections indicate an increase in soil losses across the Andes Cordillera with less severe impacts on lowlands commonly used for crops and livestock. Contrastingly, soil erosion rates tended to decrease by the end of the century in northern SA due to the reduced precipitation projected by most climate models. Overall, the most significant impacts on soil erosion caused by climate change were found for the last time slice (2071–2099) in the RCP 8.5 scenario, with mixed results, i.e., an increase in some regions and a decrease in others.

Classification of daily heavy precipitation patterns and associated synoptic types in the Campania Region (southern Italy)

Using a 20-year (2002−2021) dataset of daily precipitation collected by 107 rain gauges in the period from October to May, this study introduces a classification of the main heavy precipitation spatial patterns for the Campania Region (southern Italy). To pursue this aim, we apply a cluster analysis on the most relevant principal modes extracted from a principal component analysis of the between-day correlation matrix. The characteristics of the identified patterns, as well as their interannual and monthly distribution, are presented and discussed. Moreover, using global and regional reanalysis products, we have determined the large-scale and mesoscale atmospheric circulation types associated with heavy precipitation patterns.The heavy precipitation episodes are generally triggered by an upper level trough approaching the southern Italy from west and promoting a very moist southwesterly flow. They have been clustered into six different patterns. The first four exhibit a rainfall amount distribution strongly connected with the orography of the investigated region. In such scenarios, the orographic lifting, the low-level wind convergence induced by the orography and the transport of moisture from local sources (the western and the southern Mediterranean) and from distant regions (the Atlantic and the Africa tropical areas) can be regarded as the primary forcing of heavy rainfall. In the other two patterns, the highest precipitation is generally observed in the coastal areas (Gulfs of Naples and Salerno) and in the northwestern side of the region (Caserta district), respectively. In such circumstances, the abundant precipitation is closely linked to convective activity over the Tyrrhenian Sea, which is sustained by a low-level convergence and, in the sixth pattern, by a moisture plume coming from the tropics.The results of this study provide new insights about the links between torrential precipitation spatial distribution and atmospheric circulation schemes in the southern Italy and promise to add a useful contribution for civil protection activities related to the management of environmental risks.

Spatiotemporal variation of potential evapotranspiration and meteorological drought based on multi-source data in Nepal

Quantifying the spatiotemporal dynamic of potential evapotranspiration (PET) in topographically complex country are often limited by the scarcity and difficulty in obtaining ground-based measured climate data. Remote sensing products have multiple advantage, which allows rapid acquisition of information and status over large areas and long time series. This study compared and explored the spatiotemporal variation of PET across Nepal using three widely-used remote sensing and reanalysis data PET products (CRU_PET, MODIS_PET and PML_PET), and PET estimation using Hargreaves-Samani method based on CHIRTS datasets. On the basis of this, the spatiotemporal dynamic at seasonal and annual level and the periodical change of PET and meteorological aridity index AI (precipitation P/PET) were identified using Mann-Kendall test and Morlet Wavelet method. These PET products showed general spatial consistency over most areas in Nepal with higher PET in the south and less in the north. However, great differences were also found in PET values for four different products. Compared with the three remote sensing and reanalysis data products, CHIRTS_PET performs better in Nepal. The AI based on CHIRTS_PET showed an insignificant increasing (wetting) trend in the western and southern regions, while an insignificant decreasing (drying) trend in the central and northeastern region, which is generally consistent with the spatial pattern of precipitation. In the western and southern regions, the positive impact of increasing precipitation on AI masked the negative effect of increasing PET, and thus making this region getting humid. However, in the central and northeastern region, the increase in PET aggravates the impact of reduced precipitation on the drying trend. Our investigation have broad implications for sustainable water resources management under climate change in a topographically complex country like Nepal.

Evaluation of NEX-GDDP-CMIP6 in simulation performance and drought capture utility over China – based on DISO

Abstract Global climate models (GCMs) are the state-of-the-art tool for understanding climate change and predicting future. However, little research has been reported on the latest NEX-GDDP-CMIP6 product in China. The purpose of this study was to evaluate the simulated performance and drought capture utility of the NEX-GDDP-CMIP6 over China. First, the simulation skills of the 16 GCMs in NEX-GDDP-CMIP6 was evaluated by the 'DISO', a big data evaluation method. Second, the DISO framework for drought identification was constructed by coupling the Correlation Coefficient (CC), False Alarm Rate (FAR) and Probability of Detection (POD). Then, it was combined with SPI and SPEI to evaluate the drought detection capability of NEX-GDPD-CMIP6. The result shows that: (1) NEX-GDPD-CMIP6 can reproduce the spatial distribution pattern of historical precipitation and temperature, which performs well in simulating warming trend but fails to capture precipitation's fluctuation characteristics. (2) The best performing model in precipitation is ACCESS-CM2 (DISO 1.630) and in temperature is CESM2 (DISO 3.246). (3) The 16MME performs better than the best single model, indicating that multi-model ensemble can effectively reduce the uncertainty inherent in models. (4) The SPEI calculated by 16MME identifying drought well in arid, while SPI is recommended for other climate classifications of China.

Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil

Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project rises as a convenient yet few exploited alternative. Precisely, this study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward’s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.

Possible influence of the interdecadal variation of the extratropical southern Indian Ocean SST on East Asian summer monsoon precipitation

Observations indicate that East Asian summer precipitation exhibits significant interdecadal variability, with an alternating reverse phase distribution of rainfall from south to north, which is different from the consistent increase or decrease of precipitation in the whole region. The East Asian summer monsoon mainly affects the spatial pattern of meridional tripole precipitation on the interdecadal timescale. Moreover, the interdecadal sea surface temperature variability in the extratropical southern Indian Ocean is the most significant in the boreal summer. When there is anomalous warming in the extratropical southern Indian Ocean, the Somali cross-equatorial flow weakens and then alternating positive and negative waves propagate to central East Asia. In contrast, the New Guinea cross-equatorial flow experiences interdecadal enhancement with cyclonic circulation anomalies over the Northwest Pacific and anticyclonic circulation anomalies over the Philippines, which leads to a weakening of the South China Sea monsoon trough. On one hand, the cyclonic-anticyclonic coupling over the Philippines and Northwest Pacific results in an anomalous eastward transport of water vapor and reduced water vapor input over southern East Asia. On the other hand, the coupling strengthens the water vapor transport from the mid-latitude Northwest Pacific to central East Asia. These circulation anomalies favor the development of a meridional tripole pattern, i.e. a “negative – positive – negative” pattern of East Asian summer monsoon precipitation anomalies on the interdecadal timescale, which is specifically manifested in the abnormal increase of precipitation in central East Asia and the decrease of precipitation in northern and southern East Asia.

Spatial coherence patterns of extreme winter precipitation in the U.S.

Extreme precipitation events have a significant impact on life and property. The U.S. experiences huge economic losses due to severe floods caused by extreme precipitation. With the complex terrain of the region, it becomes increasingly important to understand the spatial variability of extreme precipitation to conduct a proper risk assessment of natural hazards such as floods. In this work, we use a complex network-based approach to identify distinct regions exhibiting spatially coherent precipitation patterns due to various underlying climate mechanisms. To quantify interactions between event series of different locations, we use a nonlinear similarity measure, called the edit-distance method, which considers not only the occurrence of the extreme events but also their intensity, while measuring similarity between two event series. Using network measures, namely, degree and betweenness centrality, we are able to identify the specific regions affected by the landfall of atmospheric rivers in addition to those where the extreme precipitation due to storm track activity is modulated by different mountain ranges such as the Rockies and the Appalachians. Our approach provides a comprehensive picture of the spatial patterns of extreme winter precipitation in the U.S. due to various climate processes despite its vast, complex topography.

High-resolution climate projection over the Tibetan Plateau using WRF forced by bias-corrected CESM

The Tibetan Plateau (TP) has undergone significant climate warming with a stronger amplitude than that experienced elsewhere in the Northern Hemisphere during the past years, but it is still challenging for most regional climate models to realistically simulate the present-day climate and promisingly project the future climate over the TP. In this study, high-resolution simulation using the Weather Research and Forecasting model (WRF) driven by bias-corrected CESM is conducted from 1979 to 2100, with the period from 2006 to 2100 under RCP4.5 and RCP8.5 (Representative Concentration Pathways) scenarios. The simulated present-day climate is evaluated firstly and then the future climate is studied secondly. The results show that compared with station observation, WRF successfully captures the spatial pattern of annual mean surface air temperature (T2m) and precipitation over the TP, with the spatial correlation coefficients larger than 0.95 for T2m and larger than 0.70 for precipitation. However, great underestimation of T2m over the southeastern TP is found in the cold season which is related to the underestimation of snow there, and the snow-temperature positive feedback develops. WRF shows limited ability in reducing the dry bias in summer, which is related to the simulated weaker water vapor transport over the southern and eastern TP. For the future changes, substantial warming, general increase in precipitation and decrease in snow are projected under RCP8.5. The warming magnitude is greater over the western TP where the more significant decrease of snow will occur by the end of 21st century. Projected precipitation tends to consistently decrease over the western TP and along the south flank of TP which is related to the low-level circulation change. The occurring frequency of light precipitation will decrease while that of non-precipitation and extreme precipitation will increase especially in the far future, with the more obvious change occurring under RCP8.5. Overall, WRF shows high ability in simulating the present-day climate and thus reliable performance in future climate projection.

Improving Precipitation Forecasts with Convolutional Neural Networks

Abstract A machine learning method based on spatial convolution to capture complex spatial precipitation patterns is proposed to identify and reduce biases affecting predictions of a dynamical model. The method is based on a combination of a classification and dual regression model approach using modified U-Net convolutional neural networks (CNN) to post-process daily accumulated precipitation over the US west coast. In this study, we leverage 34 years of high resolution deterministic Western Weather Research and Forecasting (West-WRF) precipitation reforecasts as training data for the U-Net CNN. The data is split such that the test set contains 4 water years of data that encompass characteristic west coast precipitation regimes: El Niño, La Niña, and dry and wet El Niño/Southern Oscillation (ENSO-neutral) water years. On the unseen 4-year data set, the trained CNN yields a 12.9-15.9% reduction in root mean square error (RMSE) and 2.7-3.4% improvement in Pearson correlation (PC) over West-WRF for lead times of 1-4 days. Compared to an adapted model output statistics correction, the CNN reduces RMSE by 7.4-8.9% and improves PC by 3.3-4.2% across all events. Effectively, the CNN adds more than a day of predictive skill when compared to West-WRF. The CNN outperforms the other methods also for the prediction of extreme events, which we define as the top 10% of events with the greatest average daily accumulated precipitation. The improvement over West-WRF’s RMSE/PC for these events is 19.8-21.0%/4.9-5.5% and MOS’s RMSE/PC is 8.8-9.7%/4.2-4.7%. Hence, the proposed U-Net CNN shows significantly improved forecast skill over existing methods, highlighting a promising path forward for improving precipitation forecasts.

Spatiotemporal Trends and Variation of Precipitation over China’s Loess Plateau across 1957–2018

Better understanding of the spatiotemporal characteristics of precipitation is essential in developing the best management practices for ecological restoration and soil erosion control on China’s Loess Plateau, an arid and semiarid region with severe soil erosion. This study aimed to explore the spatiotemporal trends of precipitation using long-term precipitation data from 1957 to 2018 from the 100 national standard meteorological stations across the Loess Plateau. The nonparametric Mann–Kendall statistical test and geospatial interpolation were used to detect the trends and to analyze spatial patterns of annual and seasonal precipitation. Wavelet analysis was applied to examine periodical variation across the plateau. The results reveal that regional annual precipitation over the Loess Plateau decreased during the 62 years of the study at a mean rate of −1.05 mm/10 years (p > 0.1). The changes in annual precipitation showed a periodical fluctuation with an increasing trend from 1957 to 1969, a decreasing trend from 1970 to 1999, and an increasing trend again in the first 18 years of the 21st century. The annual precipitation decreased in all eight sub-annual periods except for winter. Spatially, a decreasing trend occurred in the southern and eastern parts of the Loess Plateau, whereas a slight increase existed in the northwest in all periods. The decrease in six stations was statistically significant (p < 0.05), and a significant increase occurred in four stations (p < 0.1). These changes can be explained by an evident southward shift of the precipitation isohyets especially for 350 mm, 450 mm and 550 mm from the 1960s to the 1990s, and a clearly northward shift after the 1990s. Findings from this study facilitate an understanding of the spatial temporal trends of precipitation so appropriate countermeasures can be developed for effective vegetation restoration and soil erosion control across the Loess Plateau.

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Abstract. Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

Description and Evaluation of a New Deep Convective Cloud Model Considering In‐Cloud Inhomogeneity

AbstractConvections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in‐cloud inhomogeneities, a new convective cloud model explicitly considering in‐cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub‐plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single‐column tests show a stronger mass flux profile and low‐level detrainment with the new model. In global simulations, the long‐lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is over‐enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.

Revealing historical observations and future projections of precipitation over Northwest China based on dynamic downscaled CMIP6 simulations

The warming climate driven by global change has great potential in altering regional and global hydrologic cycles, thus leading to considerable changes in spatial variability and temporal pattern of precipitation. Northwest China (NW) has witnessed a significant wetting trend over the past decades, while the persistence of this wetting trend and potential changes in precipitation under future climate impacts remains elusive. In this study, long-term meteorological observations were used to probe historical variations of precipitation from 1951 to 2020, and the WRF model was employed as a regional climate model to examine future precipitation patterns over NW. Two 9-year downscaled WRF simulations were conducted comprising of historical (WRF-HIST; 2012–2020) and future climate change scenarios (WRF-SSP585; 2047–2055) using bias-corrected global climate model outputs from Coupled Model Intercomparison Project Phase 6 (CMIP6). Compared with ground observations, the WRF model exhibited strong capability in capturing the spatial pattern and temporal variations of precipitation across the NW. Intense precipitation was mainly found in stations located at northern NW and southeastern NW. Summertime precipitation substantially contributed to annual precipitation over the study region. Future precipitation projections suggest significant decreases of precipitation across the southern and eastern NW, with a stronger reduction magnitude in summer. Further, extreme precipitation events were projected to decrease in spring and summer, suggesting that the NW may become drier and the wetting trend may shift to another pattern in the 2050s under the SSP585 climate scenario. Overall, this study reveals historical and future potential changes in precipitation over NW through a high-resolution, dynamically downscaled dataset from WRF modeling, which in turn will help inform regional mitigation and adaption on potential impacts of future climate change on NW.

Enhanced precipitation prediction using DEM‐based predictors and satellite imagery

AbstractOver the last decades, climatic changes have triggered considerable impacts across the globe with detrimental effects on all ecosystems. Given the complexity of topography and climate, Romania is one of the most exposed countries in the South‐Eastern Europe to extreme hydrological events. As a consequence, the spatial distribution of precipitation is of greater importance for future analysis. This study examines the performance of Empirical Bayesian Kriging Regression Prediction (EBKRP) and Geographically Weighted Regression (GWR) to predict the spatial distribution of annual and seasonal precipitation over Romania. Twelve co‐variables derived from topography, and remote sensing products data were used to improve the prediction. The co‐variable selection process was performed prior to the regression model to reduce the complexity and processing time, using the Boruta Algorithm (BA), which is an innovative approach. The performance of BA was compared with Gini Coefficient. Our findings confirmed that 10 co‐variables were relevant to predict annual precipitation and nine for seasonal. The overall prediction of precipitation is more influenced by topography (altitude, slope, surface roughness) and the distance to marine bodies (Black Sea and Adriatic Sea). Cross‐validation and five statistical metrics were applied to assess the performance of the regression models. The results show similar spatial distribution pattern of precipitation, whereas the highest annual precipitation is found in the Carpathian Mountains (EBKRP: 1,399.2 mm, GWR: 1,249.7 mm) and the lowest in the lowlands (EBKRP: 355.9 mm, GWR: 346.8 mm). For seasons, both methods predicted the highest precipitation in summer and lowest in autumn. In all seasons, the precipitation is underestimated by both methods, however, for annual, only GWR does so. Our study revealed GWR as the best method to predict annual and seasonal precipitation over Romania, as it yielded the highest correlation coefficient Spearman (S), Pearson (P), determination coefficient (R2) and lowest mean absolute error (MAE) and root mean square error (RMSE).

To what extent horizontal resolution improves the simulation of precipitation in CMIP6 HighResMIP models over Southwest China?

Southwest China (SWC) is located in the eastern part of Tibetan Plateau (TP) with large elevation differences and complex topography, which has always been a challenge to the simulation of precipitation in climate modeling community. In this study, the differences in the simulation of precipitation over the SWC are evaluated using the lower and higher resolution models (LR and HR) from the High–Resolution Model Intercomparison Project (HighResMIP) protocol in Coupled Model Intercomparison Project Phase 6 (CMIP6). Our results indicate that the spatial patterns of annual precipitation over the SWC for the period 1985–2014 are well reproduced in most of the HR and LR models, with an increasing tendency from the northwest to southeast. Compared with LR models, the wet biases over the eastern TP and the dry biases over the Sichuan Basin are significantly reduced in HR models. The bias for annual precipitation of the multi–model ensemble mean (MME) has been reduced from 0.97 mm/day (LR) to 0.72 mm/day (HR). In addition, the simulation of extreme precipitation is significantly improved in the finer horizontal resolution models, showing effectively reduced simulation biases in the Sichuan Basin compared with the LR models. The frequency and intensity of extremes are represented by heavy precipitation days (R10 mm) and maximum consecutive 5 days precipitation (Rx5day), which the relative changes have been decreased from 66% (LR) to 47% (HR) in R10 mm and decreased from 23% (LR) to 19% (HR) in Rx5day. We further examine the possible reasons for the difference between LR and HR models in precipitation simulation, showing that the HR models could generate “additional” cyclonic circulation and promote more upward motion with the water vapor convergence, thus correcting the dry biases of precipitation simulation over the Sichuan Basin. This indicates that atmospheric circulation and moisture conditions could be simulated more realistically in climate model with a finer resolution, further improving precipitation simulation performance.

Extreme precipitation changes over the Yangtze River Basin in 1901-2020

To better understand the characteristics of long-term change and variability in regional extreme precipitation and to examine possible regional responses to global climate warming, we analyzed temporal and spatial patterns of precipitation and extreme precipitation index changes in the Yangtze River Basin (YRB) over the last 120 yr. Based on the China Meteorological Administration’s daily precipitation data set of 60 city stations in mainland China from 1901-2020, we found that total annual precipitation and daily precipitation intensity of the Upper Reaches (UR) experienced a downward trend, but those of the Middle and Lower Reaches (MLR) showed an upward trend. Precipitation amount and intensity increased, while the number of precipitation days slightly decreased over the YRB during 1901-2020. Most extreme precipitation indices showed a decreasing trend in the first 60 yr that reversed after 1961 into slightly upward trends, except for consecutive dry days and wet days in the YRB during 1901-2020. There were significant downward trends for consecutive dry days and maximum 1 and 5 d precipitation during the first 60 yr, and significant upward trends for maximum 1 d precipitation, total extreme heavy precipitation, and very heavy precipitation days in the last 60 yr. The trends for extreme precipitation in the UR were lower than those in the MLR during 1901-2020, particularly in the last 60 yr, whereas the trends in the UR were higher than those in the MLR during 1901-1960. The fluctuations of extreme precipitation indices in the UR were more dramatic than those in the MLR, and the extreme precipitation indices all showed 5-10 or 20 yr periodic variabilities. The analysis also showed that observed changes in regional extreme events were directly related to rapid urbanization around the stations and multi-decadal variability of the East Asian monsoon.

Evaluation of CMIP6 GCMs performance to simulate precipitation over Southeast Asia

The performance for simulating precipitation of 27 global climate models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) is evaluated over the Southeast Asia (SEA) region and nine SEA countries for the period 1975–2014 by comparing observation data from the Global Precipitation Climatology Centre (GPCC). The performance ranking of the GCMs was conducted with the ranking scores method using eight performance metrics that cover both spatial and temporal patterns. Compared to GPCC, the results show that some GCMs in CMIP6 reasonably capture the spatial precipitation patterns, with more in maritime and less in mainland. However, the output of most models presents considerable overestimation (wet bias) in Thailand, Cambodia, central Myanmar and maritime countries, and underestimation (dry bias) in Indonesia, Lao, northern Vietnam and western Myanmar. In addition, the findings illustrate that many models can reproduce the annual cycle shape and inter-annual variability which are consistent with GPCC; however, only 2 out of 27 models can detect increasing trends such as GPCC in every study domain. By model ranking, the best models vary from area domain to area domain. TaiESM1 performs best among the 27 GCMs over SEA region as well as Thailand. The model that has the best performance in most counties, i.e., Cambodia, Indonesia, Lao, and Vietnam is EC-Earth3-Veg-LR. EC-Earth3 is the best model in Brunei, Malaysia while CESM2-FV2 is the best model in Myanmar and the Philippines. It is also discovered that the mean ensemble of all GCMs has limited skills for all study domains. The results of this study can be used to support selection of the suitable models for simulating precipitation in specific study domains.

Spatial patterns of malaria case burden and seasonal precipitation in India during 1995-2013.

The majority of malaria cases in Southeast Asia occur in India. It is a major public health problem in India, which accounts for substantial morbidity, mortality, and economic loss. The spatial distribution of malaria widely varies due to geo-ecological diversity, multi-ethnicity, and wide distribution of the different anopheline vectors. The predominant malaria parasites in India for malaria are P. Falciparum (Pf) and P. Vivax (Pv). This study analyzes the spatial patterns of malaria cases, specifically the two dominant malaria vectors, at the regional level and its relation to seasonal precipitation. The results of our study revealed an overall decline in malaria cases in the later years. The spatial spread of malaria cases was more widespread during the normal monsoon years vs drought years, which can be attributed to more conducive environment for mosquitos to breed. The correlation analysis revealed a stronger correlation between malaria case burden and monsoon precipitation. Spatially, the strongest correlation between seasonal and annual precipitation, and malaria case burden were located across the northern plains and northeast India. The results of this research further our understanding of the relationship between seasonal precipitation and malaria case burden at the regional level across India.

Performance of a regional climate model with interactive vegetation (REMO-iMOVE) over Central Asia

The current study evaluates the regional climate model REMO (v2015) and its new version REMO-iMOVE, including interactive vegetation and plant functional types (PFTs), over two Central Asian domains for the period of 2000–2015 at two different horizontal resolutions (0.44° and 0.11°). Various statistical metrices along with mean bias patterns for precipitation, temperature, and leaf area index have been used for the model evaluation. A better representation of the spatial pattern of precipitation is found at 0.11° resolution over most of Central Asia. Regarding the mean temperature, both model versions show a high level of agreement with the validation data, especially at the higher resolution. This also reduces the biases in maximum and minimum temperature. Generally, REMO-iMOVE shows an improvement regarding the temperature bias but produces a larger precipitation bias compared to the REMO conventional version with interannually static vegetation. Since the coupled version is capable to simulate the mean climate of Central Asia like its parent version, both can be used for impact studies and future projections. However, regarding the new vegetation scheme and its spatiotemporal representation exemplified by the leaf area index, REMO-iMOVE shows a clear advantage over REMO. This better simulation is caused by the implementation of more realistic and interactive vegetation and related atmospheric processes which consequently add value to the regional climate model.

Application of bias corrected FGOALS-g3 model products for detecting changes in extreme precipitation in the Tienshan Mountains, Central Asia

This paper investigates the performance of bias-corrected Flexible Global Ocean-Atmosphere-Land System-g3 (FGOALS-g3) model products and then detects changes in extreme precipitation (EP) in the Tienshan Mountains, Central Asia (TMCA), as reflected by 25 EP indices. The reliability of the FGOALS-g3 model outputs is assessed systematically based on multiple statistical indicators against multi-source precipitation datasets and the bias-corrected FGOALS-g3 model products are applied to project EP variations under different global warming levels. Using the geographical detector method, a novel statistical method for detecting spatial heterogeneity and elucidating the underlying causes, the explanatory power of 20 atmospheric circulation factors related to EP is examined. The findings indicate that while the FGOALS-g3 products can detect the spatial pattern of multi-year average precipitation in the TMCA, there is an obvious overestimation in magnitude, especially in the West and Middle Tienshan Mountains. These biases are significantly reduced, however, after downscaling and bias correction. Compared with the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) data, the grid-by-grid error in bias-corrected FGOALS-g3 simulated mean precipitation is between −7.64% and 10.95%. In terms of EP, the corrected FGOALS-g3 products not only reproduce the spatial distribution, but also reasonably simulate their magnitudes, with some overestimation in light EP and underestimation in heavy EP. Overall, across the historical period, EP has increased. The intensity and frequency of EP are projected to generally increase under different scenarios. At 1.5 °C warming levels, annual total precipitation in wet days (PRCP) increases by 5.74% (7.74%) under the SSP245 (SSP585). Additionally, as an EP becomes rarer, its rate of change rises. The main driving factors in EP are detected to be 30 hPa zonal wind (30ZW), relative number of sunspots (SF), south Asian summer monsoon (SAM), sea surface temperature anomaly in the region of 5°S-5°N, 170°-120°W (NINO 3.4), and mean surface temperature (T).