Seasonality of the hydrological cycle in major South and Southeast Asian River Basins as simulated by PCMDI/CMIP3 experiments

Hasson, Shabeh ul; Lucarini, Valerio; Pascale, Salvatore; Böhner, Jürgen

doi:10.5194/esdd-4-627-2013

Cited by 10 publications

(26 citation statements)

References 54 publications

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“…Nepal has four main seasons, such as, post‐monsoon (October–November), winter (December–February), pre‐monsoon (March–May), and monsoon (June–September). Dry and cold westerly winds in the upper troposphere influence the study area during non‐monsoon months, whereas monsoon season is characterized by warm and moist southerly to south‐easterly near‐surface flow conditions (Böhner and Lehmkuhl, ; Hasson et al ., ; Gerlitz et al ., ; Hasson, ; Kadel et al ., ). Post‐monsoon is the driest season with mainly clear sky conditions, whereas winter is the coldest season.…”

Section: Study Areamentioning

confidence: 99%

Rising mean and extreme near‐surface air temperature across Nepal

Karki

Hasson

Gerlitz

et al. 2019

Intl Journal of Climatology

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Owing to unique topographic and ecological diversity, central Himalayan state of Nepal is exposed to adverse impacts of climate change and associated disasters. However, countrywide historical assessment of mean and extreme temperature changes, a prerequisite for devising adequate adaptation strategies, is still lacking. Here, we present a comprehensive picture of mean and extreme temperature trends across Nepal over the 1980–2016 period, based on high‐quality daily temperature observations from 46 stations. Our results suggest that besides winter cooling in southern lowlands, the country features a widespread warming, which is higher for maximum temperature (~0.04°C⋅year−1) than for minimum temperature (~0.02°C⋅year−1), over the mountainous region than in valleys and lowlands and during the pre‐monsoon season than for the rest of the year. Consistently, we found a higher increasing trend for warm days (13 days⋅decade−1) than for warm nights (4 days⋅decade−1), whereas the rates of decrease for cold days and cold nights are the same (6 days⋅decade−1). Further investigations reveal that pronounced warming in maximum temperature over mountain regions can be attributed to less cloud cover and snowfall in recent decades during non‐monsoon seasons as a result of positive geopotential height anomalies and strengthening of anticyclonic circulations in the mid‐to‐upper troposphere. Similarly, increased stability of lower atmosphere during winter and post‐monsoon seasons caused prolonged and frequent periods of fog over lowlands, resulting in significant winter cooling there.

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Section: Study Areamentioning

confidence: 99%

Rising mean and extreme near‐surface air temperature across Nepal

Karki

Hasson

Gerlitz

et al. 2019

Intl Journal of Climatology

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“…It has to be noted however that different measures of the wet season duration which are not based on integral properties of the rainfall distribution but on local properties -e.g. retreat minus onset dates (Sperber et al, 2013;Kitoh et al, 2013;Hasson et al, 2014), where onset and retreat are defined by the 5 mm day −1 threshold -may give different results. Within this framework, let us also introduce another useful statistical indicator of rainfall seasonality, the centroid.…”

Section: A2 Relative Entropy and The Spread Of P Mmentioning

confidence: 99%

“…The latter will be the topic of this study. A complete description of rainfall seasonality needs to quantify the duration of the wet and dry seasons, their intensity and their timing (Chou et al, 2013;Noake et al, 2012;Sperber et al, 2013;Hasson et al, 2014).…”

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confidence: 99%

Analysis of rainfall seasonality from observations and climate models

et al. 2014

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Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere-ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring one maximum in the monthly rain distribution, it is related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. CMIP5 models consistently overestimate the RE over tropical Latin America and underestimate it in Western Africa and East Asia. It is demonstrated that positive RE biases in a GCM are associated with simulated monthly precipitation fractions which are too large during the wet months and too small in the months preceding the wet season; negative biases are instead due to an excess of rainfall during the dry months.Comment: 35 pages, 15 figure

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“…However, due to erroneous conceptualization systematic errors in RCMs are observed during spatial averaging of grid cells (Teutschbein and Seibert, 2010). Underrepresentation of existing physio-geographical characteristics results in a serious bias in crucial parameters such as temperature and precipitation (Christensen et al, 2008;Hasson et al, 2014). Therefore, bias correction of the RCM outputs for hydrologic impact assessment is essential (Wood et al, 2004;Ines and Hansen, 2006;Teutschbein and Seibert, 2012;Nguyen et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Regional changes of precipitation and temperature over Bangladesh using bias‐corrected multi‐model ensemble projections considering high‐emission pathways

Fahad

Islam

Nazari

et al. 2017

Intl Journal of Climatology

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A multi‐model ensemble provides useful information about the uncertainty of the future changes of climate. High‐emission scenarios using representative concentration pathways (RCP8.5) of the Fifth Phase Coupled Model Inter‐comparison Project (CMIP5) in the Intergovernmental Panel on Climate Change (IPCC) also aids to capture the possible extremity of the climate change. Using the CMIP5 regional climate modelling predictions, this study analyses the distribution of the temperature and precipitation in Bangladesh in the recent years (1971–2000) and in three future periods (2010–2040, 2041–2070 and 2070–2100) considering RCP8.5 scenarios. Climate changes are expressed in terms of 30‐year return values of annual near‐surface temperature and 24‐h precipitation amounts. At the end of the century, the mean temperature increase over Bangladesh among the 11 RCMs will vary from 5.77 to 3.24 °C. Spatial analysis of the 11 RCMs exhibited that the southwest and the south central parts of Bangladesh will experience a greater temperature rise in the future. Possible changes in rainfall are also exhibited both temporally and spatially. Based on the analysis of all the RCMs, a significant increase of rainfall in the pre‐ and post‐monsoon period is observed. It is also evident that monsoon rainfall will not increase in comparison with pre‐monsoon season. Zonal statistics of 64 districts of Bangladesh are also conducted for the 2020s, 2050s and 2080s to find out the most exposed regions in terms of the highest rise in temperature and changes in precipitation.

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Seasonality of the hydrological cycle in major South and Southeast Asian River Basins as simulated by PCMDI/CMIP3 experiments

Cited by 10 publications

References 54 publications

Rising mean and extreme near‐surface air temperature across Nepal

Rising mean and extreme near‐surface air temperature across Nepal

Analysis of rainfall seasonality from observations and climate models

Regional changes of precipitation and temperature over Bangladesh using bias‐corrected multi‐model ensemble projections considering high‐emission pathways

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