This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall ...climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization.
This study examined the rainfall hindcast of 15 CMIP6 GCMs in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh considering ERA5 reanalysis as the reference dataset. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall except for winter. Like ERA5, the GCMs showed an increasing trend in pre‐monsoon rainfall and a declining trend in post‐monsoon rainfall, but a monsoon rainfall trend that was the polar opposite (negative) of the ERA5 trend (positive). The MME of the GCMs performed better than the individual GCMs in reconstructing rainfall climatology, temporal variability, and trends. The most effective GCMs in reproducing rainfall climatology over Bangladesh were found to be MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4.
Multiple observational data sets and atmosphere‐only simulations from the Coupled Model Intercomparison Project Phase 5 are analyzed to characterize recent rainfall variability and trends over Africa ...focusing on 1983–2010. Data sets exhibiting spurious variability, linked in part to a reduction in rain gauge density, were identified. The remaining observations display coherent increases in annual Sahel rainfall (29 to 43 mm yr−1 per decade), decreases in March–May East African rainfall (−14 to −65 mm yr−1 per decade), and increases in annual Southern Africa rainfall (32 to 41 mm yr−1 per decade). However, Central Africa annual rainfall trends vary in sign (−10 to +39 mm yr−1 per decade). For Southern Africa, observed and sea surface temperature (SST)‐forced model simulated rainfall variability are significantly correlated (r~0.5) and linked to SST patterns associated with recent strengthening of the Pacific Walker circulation.
Key Points
Rainfall trends from multiple observational data sets are not consistent in some regions
Spurious rainfall trends are linked to changes in gauge density over time
SST patterns play a strong role in determining Africa‐wide rainfall trends since 1983
Severe water erosion occurs during extreme storm events. Such an exceedingly severe storm occurred in Zhengzhou in central China on 20 July 2021 (the 7.20 storm). The magnitude and frequency of ...occurrence of this storm event were examined in terms of how erosive it was. To contextualize this extreme event, hourly rainfall data from 2420 automatic meteorological stations in China from 1951 to 2021 were analyzed to (1) characterize the spatial and temporal distribution of the rainfall amount and rainfall erosivity of the 7.20 storm, (2) evaluate the average recurrence interval of the maximum daily and event rainfall erosivity, and (3) establish the geographical distribution of the maximum daily and event rainfall erosivity in China. The center of the 7.20 storm moved from southeast to northwest in Henan Province, and the most intense period of rainfall occurred in the middle and late stages of the storm. Zhengzhou Meteorological Station happened to be aligned with the center of the storm, with a maximum daily rainfall of 552.5 mm and a maximum hourly rainfall intensity of 201.9 mm h−1. The average recurrence intervals of the maximum daily rainfall erosivity (43 354±1863 MJ mm ha−1 h−1) and the maximum event rainfall erosivity (58 874±2351 MJ mm ha−1 h−1) were estimated to be about 19 200 and 53 700 years, respectively, assuming the log-Pearson type-III distribution, and these were the maximum rainfall erosivities ever recorded among 2420 meteorological stations in mainland China up to 2022. The 7.20 storm suggests that the most erosive of storms does not necessarily occur in the wettest places in southern China, and these can occur in mid-latitude around 35∘ N with a moderate mean annual rainfall of 566.7 mm in Zhengzhou.
Rainfall is one of the most important environmental variables. However, it is a challenge to measure it accurately over space and time. During the last decade, commercial microwave links (CMLs), ...operated by mobile network providers, have proven to be an additional source of rainfall information to complement traditional rainfall measurements.
In this study, we present the processing and evaluation of a German-wide data set of CMLs.
This data set was acquired from around 4000 CMLs distributed across Germany with a temporal resolution of 1 min. The analysis period of 1 year spans from September 2017 to August 2018. We compare and adjust existing processing schemes on this large CML data set. For the crucial step of detecting rain events in the raw attenuation time series, we are able to reduce the amount of misclassification. This was achieved by using a new approach to determine the threshold, which separates a rolling window standard deviation of the CMLs' signal into wet and dry periods. For the compensation for wet antenna attenuation, we compare a time-dependent model with a rain-rate-dependent model and show that the rain-rate-dependent model performs better for our data set. We use RADOLAN-RW, a gridded gauge-adjusted hourly radar product from the German Meteorological Service (DWD) as a precipitation reference, from which we derive the path-averaged rain rates along each CML path.
Our data processing is able to handle CML data across different landscapes and seasons very well. For hourly, monthly, and seasonal rainfall sums, we found good agreement between CML-derived rainfall and the reference, except for the winter season due to non-liquid precipitation. We discuss performance measures for different subset criteria, and we show that CML-derived rainfall maps are comparable to the reference.
This analysis shows that opportunistic sensing with CMLs yields rainfall information with good agreement with gauge-adjusted radar data during periods without non-liquid precipitation.
This study provides an updated analysis of the evolution of seasonal rainfall intensity in the Amazon basin, considering the 1981–2017 period and based on HOP (interpolated HYBAM observed ...precipitation) and CHIRPS (The Climate Hazards Group Infrared Precipitation with Stations) rainfall data sets. Dry and wet day frequencies as well as extreme percentiles are used in this analysis, producing the same results. Dry-day frequency (DDF) significantly increases in the Southern Amazon (p < 0.01), particularly during September–November (SON) in the Bolivian Amazon, central Peruvian Amazon and far southern Brazilian Amazon. Consistently, total rainfall in the southern Amazon during SON also shows a significant diminution (p < 0.05), estimated at 18%. The increase in SON DDF in the southern Amazon is related to a warming of the northern tropical Atlantic Ocean and a weakening of water vapour flux from the tropical Atlantic Ocean. The increase in DDF in the southern Amazon is related to enhanced wind subsidence (ascendance) over the 10°S–20°S (5°S–5°N) region and to a deficit (excess) of specific humidity at 1000–300 hPa south of 10°S (north of the 5°S), which suggest a reduction of deep convection over southern Amazonia. Subsidence over the southern Amazon shows a significant trend (p < 0.01), which can explain the significant increase in DDF. Wet-day frequency (WDF) significantly increases in the northern Amazon, particularly during the March–May (MAM) period (p < 0.01), producing an estimated rainfall increase during MAM of 17% (p < 0.01) between 1981 and 2017. Significant changes in both WDF and rainfall in northern Amazon have been detected in 1998 (p < 0.01). After 1998, the increase in MAM WDF and rainfall is explained by enhanced moisture flux from the tropical North Atlantic Ocean and an increase in deep convection over the northern and northwestern Amazon. These evolutions in DDF and WDF and in the tropical atmosphere occur simultaneously with an increase in sea surface temperature in the northern Atlantic Ocean, particularly after the mid-1990s. These results provide new insight into rainfall variability and climatic features related to increasing dry season length in southern Amazonia. Severe recent droughts may be associated with the increase in DDF in the South. In addition, the increase in MAM rainfall intensity in northern Amazon after 1998 may be associated with several historical floods that occurred after this date.
AbstractAreal reduction factors (ARFs) are widely used to transform the point rainfall intensity to the areal rainfall intensity in engineering practice. Inappropriate ARFs may result in an ...overestimate or underestimate of the areal rainfall and consequently lead to the inappropriate design of infrastructure. This study aims to explore the differences in ARFs estimated by four empirical methods and quantitatively analyze the effect of rainfall duration, area, return period, local topography, and rain gauge density on ARFs in the coastal city Shenzhen, China. The results indicate that the original fixed-area method yields more conservative (higher) ARF estimates than the other three methods, which also consider the return period with the coefficient of variation ranging from 0.014 to 0.054. Bell’s method and its modified versions produced modest discrepancies in ARFs, with coefficient of variation (COV) values ranging from 0.008 to 0.023. A declining trend of ARFs with increasing return period was observed for six durations (1, 2, 3, 6, 36, and 48 h), whereas ARFs tended to increase with increasing return period for 12- and 24-h durations. Meanwhile, ARFs in mountainous areas (the east part of Shenzhen) were lower than that in the flat terrain in the west part with a maximum reduction of 0.13, which might be associated with the higher spatial variability of rainfall caused by the terrain effect. In addition, ARFs derived from the sparse rain gauge network may be overestimated compared with that from the dense network (maximum overestimation of 0.041). This study provides new insights into the relationship between ARFs and return periods, and highlights that ARFs should be further studied based on the up-to-date rainfall data to tackle the changing climate.
Satellite rainfall products are an important source of rainfall data in un-gauged catchments. However, these products need to be validated as their accuracy can be affected by geographical position, ...topography, climate and embedded algorithms. Eight satellite rainfall products such as African Rainfall Climatology (ARC2), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPs), Global Precipitation Climatology Project GPCP), CPC Morphic technique (CMorph), Atmospheric Administration Climate Prediction Center (NOAA-CPC) merged analysis (CMap), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Estimation Algorithm version 2 (RFEv2) and Tropical Rainfall Measurement Mission (TRMM) were evaluated against ground observations over the complex topography of the upper Tekeze-Atbara basin in Ethiopia. The accuracy of the datasets was evaluated at different temporal and larger spatial scales over the period 2002-2015. The results show that the rainfall data of CHIRPS outperformed all other products at all temporal and spatial scales. Next to CHIRPS, estimates from RFEv2, 3B42v7, and PERSIANN products are closest to the measurements at rain gauges for all spatiotemporal scales: daily, monthly and seasonal, and both at point and spatial scales. The percentage bias (PBias) and correlation coefficient (r) of these products were within ±25% and >0.5 for all scales. The remaining products performed poorly with PBias up to 200% and lower r (<0.5) at all scales. However, the performance of all products improved as the temporal scale increased to month and season at all spatial scales. Compared to low altitudes <2000 m above sea level (m.a.s.l.), the PBias at high altitude (>3000 m.a.s.l.) increased by 35% whilst r dropped by 28%. CHIRPS and 3B42v7 products showed best agreement in mountainous terrains. However, all datasets show no consistency of the error sign. CMorph and 3B42v7 consistently overestimate rainfall relative to all rain gauges during the pixel-to-point rainfall comparison approach and at lowland areas during the areal averaged rainfall comparison. The other six products showed a clear underestimation at all spatial scales. In summary, the results show that rainfall estimates by CHIRPS, RFEv2 and 3B42v7 have a consistently better agreement with ground rainfall at all spatiotemporal scales. Considering the complex topography and limited gauges, the performance of CHIRPS, RFEv2 and 3B42v7 indicates that these products can be used for hydrological and overall water management applications in the region.
The present study reveals a close relationship between the leading mode of continental U.S. (CONUS) summer rainfall and the East Asian subtropical monsoon rainfall (viz., mei-yu in China, baiu in ...Japan, and changma in the Korean peninsula). The East Asian subtropical monsoon rainfall and the CONUS dipole rainfall patterns are connected by an upper-level Asia–North America (ANA) teleconnection. The Rossby wave energy propagates along the path of the westerly jet stream (WJS) from East Asia to North America, affecting the CONUS summer rainfall. Mechanisms through which East Asian summer monsoon heating influence North American rainfall are illustrated by idealized anomaly atmospheric general circulation model experiments. In boreal winter, because of the southward shift of the WJS, the Pacific–North American (PNA) pattern can be excited by the tropical central/eastern Pacific heating associated with El Niño, affecting the rainfall over CONUS. In boreal summer, because the WJS is weaker and locates farther to the north, an equatorial heating anomaly cannot directly perturb the WJS. A perturbation heating over subtropical East Asia, however, can trigger an ANA pattern along the path of the WJS, affecting the rainfall over North America. The season-dependent teleconnection scenario illustrates that the predictability source of CONUS rainfall variability is different between winter and summer. While the PNA pattern generated by El Niño is critical for CONUS rainfall in northern winter, the CONUS dipole rainfall variation in boreal summer is mainly governed by the remote forcing over subtropical East Asia via the ANA teleconnection.
The summer rainfall amount over East China is expected to increase along with a strengthening of the East Asian summer monsoon in a warmer climate. However, how the seasonality of precipitation will ...respond to global warming remains uncertain and is closely related to monsoon circulation. Here, we project future changes in multiple intra-seasonal rainfall characteristics over East China under 1.5 °C, 2 °C, 2.5 °C, and 3 °C of global warming above pre-industrial levels based on coupled model intercomparison project phase 6 multi-model projections. Both the onset and cessation dates over South China are likely to be delayed in a warmer climate, resulting in a later shift of the rainy season. In contrast, advanced cessation dates are projected over Northeast China with high model consensus. As for rainfall characteristics within the rainy season, total rainy season rainfall is expected to increase over the whole East China domain, with remarkable enhancement of heavy rainfall intensity. Further analysis indicates that continuous warming over a 1.5 °C warmer climate is projected to further increase total rainy season rainfall and enhance heavy rainfall intensity, with a magnitude at least twice as large with additional warming of 0.5 to 1.5 °C. Also, changes in cessation dates over South and Northeast China are projected to be enhanced significantly. These results together indicate the vital need to slow down global warming to reduce potential adverse impacts on agricultural and socioeconomic development.
The Andes/Amazon transition is among the rainiest regions of the world and the interactions between large‐scale circulation and the topography that determine its complex rainfall distribution remain ...poorly known. This work provides an in‐depth analysis of the spatial distribution, variability, and intensity of rainfall in the southern Andes/Amazon transition, at seasonal and intraseasonal time scales. The analysis is based on comprehensive daily rainfall data sets from meteorological stations in Peru and Bolivia. We compare our results with high‐resolution rainfall TRMM‐PR 2A25 estimations. Hotspot regions are identified at low elevations in the Andean foothills (400–700 masl) and in windward conditions at Quincemil and Chipiriri, where more than 4000 mm rainfall per year are recorded. Orographic effects and exposure to easterly winds produce a strong annual rainfall gradient between the lowlands and the Andes that can reach 190 mm/km. Although TRMM‐PR reproduces the spatial distribution satisfactorily, it underestimates rainfall by 35% in the hotspot regions. In the Peruvian hotspot, exceptional rainfall occurs during the austral dry season (around 1000 mm in June–July–August; JJA), but not in the Bolivian hotspot. The direction of the low‐level winds over the Andean foothills partly explains this difference in the seasonal rainfall cycle. At intraseasonal scales in JJA, we found that, during northerly wind regimes, positive rainfall anomalies predominate over the lowland and the eastern flank of the Andes, whereas less rain falls at higher altitudes. On the other hand, during southerly regimes, rainfall anomalies are negative in the hotspot regions. The influence of cross‐equatorial winds is particularly clear below 2000 masl.
Key Points:
TRMM‐PR and 95 stations describe rainfall contrasts in Amazon‐Andes transition
Rainfall hotspots extreme events are related to synoptic atmospheric circulation
Rainfall day‐to‐day variability is associated with cross‐equatorial winds
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