scholarly journals Assessing the Potential Impacts of Climate Changes on Rainfall and Evapotranspiration in the Northwest Region of Bangladesh

Climate ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 94 ◽  
Author(s):  
Fazlul Karim ◽  
Mohammed Mainuddin ◽  
Masud Hasan ◽  
Mac Kirby
Keyword(s):  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Annual Rainfall ◽  
Emission Scenarios ◽  
Rabi Season ◽  
Kharif Season ◽  
Almost All ◽  
Northwest Region

Changes in the natural climate is a major concern for food security across the world, including Bangladesh. This paper presents results from an analysis on quantitative assessment of changes in rainfall and potential evapotranspiration (PET) in the northwest region of Bangladesh, which is a major agricultural hub in the country. The study was conducted using results from 28 global climate models (GCMs), based on IPCC’s 5th assessment report (AR5) for two emission scenarios. Projections were made over the period of 2045 to 2075 for 16 administrative districts in the study area, and the changes were estimated at annual, seasonal and monthly time scale. More projections result in an increase in rainfall than decrease, while almost all projections show an increase in PET. Although annual rainfall is generally projected to increase, some projections show a decrease in some months, especially in December and January. Across the region, the average change projected by the 28 GCMs for the moderate emission was an increase of 235 mm (12.4%) and 44 mm (3.4%) for rainfall and PET, respectively. Increases in rainfall and PET are slightly higher (0.6% and 0.2%, respectively) under high emission scenarios. Increases in both rainfall and PET were projected for two major cropping seasons, Kharif (May-Oct) and Rabi (Nov-Apr). Projections of rainfall show increase in the range of 160 to 250 mm (with an average of 200 mm) during the Kharif season. Although an increase is projected in the Rabi season, the amount is very small (~10mm). It is important to note that rainfall increases mostly in the Kharif season, but PET increases for both Kharif and Rabi seasons. Contrary to rainfall, increase in PET is higher during Rabi season. This information is crucial for better adaptation under increased water demand for agricultural and domestic use.

scholarly journals What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ge Peng ◽  
Jessica L. Matthews ◽  
Muyin Wang ◽  
Russell Vose ◽  
Liqiang Sun
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Adaptation ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Climate Projections ◽  
Emission Scenarios ◽  
Ice Coverage ◽  
Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

scholarly journals Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP-6 models

2020 ◽  
Author(s):  
Anja Katzenberger ◽  
Jacob Schewe ◽  
Julia Pongratz ◽  
Anders Levermann
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Models ◽  
Monsoon Rainfall ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Climate Change ◽  
Global Mean Temperature ◽  
Almost All

Abstract. The Indian summer monsoon is an integral part of the global climate system. As its seasonal rainfall plays a crucial role in India's agriculture and shapes many other aspects of life, it affects the livelihood of a fifth of the world's population. It is therefore highly relevant to assess its change under potential future climate change. Global climate models within the Coupled Model Intercomparison Project Phase 5 (CMIP-5) indicated a consistent increase in monsoon rainfall and its variability under global warming. Since the range of the results of CMIP-5 was still large and the confidence in the models was limited due to partly poor representation of observed rainfall, the updates within the latest generation of climate models in CMIP-6 are of interest. Here, we analyse 32 models of the latest CMIP-6 exercise with regard to their annual mean monsoon rainfall and its variability. All of these models show a substantial increase in June-to-September (JJAS) mean rainfall under unabated climate change (SSP5-8.5) and most do also for the other three Shared Socioeconomic Pathways analyzed (SSP1-2.6, SSP2-4.5, SSP3-7.0). Moreover, the simulation ensemble indicates a linear dependence of rainfall on global mean temperature with high agreement between the models and independent of the SSP; the multi-model mean for JJAS projects an increase of 0.33 mm/day and 5.3 % per degree of global warming. This is significantly higher than in the CMIP-5 projections. Most models project that the increase will contribute to the precipitation especially in the Himalaya region and to the northeast of the Bay of Bengal, as well as the west coast of India. Interannual variability is found to be increasing in the higher-warming scenarios by almost all models. The CMIP-6 simulations largely confirm the findings from CMIP-5 models, but show an increased robustness across models with reduced uncertainties and updated magnitudes towards a stronger increase in monsoon rainfall.

A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change

2021 ◽  
Author(s):  
Thedini Asali Peiris ◽  
Petra Döll
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Net Radiation ◽  
Ensemble Mean ◽  
The Impact

<p>Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO<sub>2</sub> concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO<sub>2</sub> and climate. This active vegetation response consists of three components. With higher CO<sub>2</sub> concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.</p><p>Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.</p><p>The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.</p><p>We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).</p><p>Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.</p>

scholarly journals Terrestrial Aridity and Its Response to Greenhouse Warming across CMIP5 Climate Models

2015 ◽  
Vol 28 (14) ◽  
pp. 5583-5600 ◽  
Author(s):  
Jacob Scheff ◽  
Dargan M. W. Frierson
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Aridity Index ◽  
Global Climate Models ◽  
Greenhouse Warming ◽  
Dimensionless Ratio ◽  
The Tropics

Abstract The aridity of a terrestrial climate is often quantified using the dimensionless ratio of annual precipitation (P) to annual potential evapotranspiration (PET). In this study, the climatological patterns and greenhouse warming responses of terrestrial P, Penman–Monteith PET, and are compared among 16 modern global climate models. The large-scale climatological values and implied biome types often disagree widely among models, with large systematic differences from observational estimates. In addition, the PET climatologies often differ by several tens of percent when computed using monthly versus 3-hourly inputs. With greenhouse warming, land P does not systematically increase or decrease, except at high latitudes. Therefore, because of moderate, ubiquitous PET increases, decreases (drying) are much more widespread than increases (wetting) in the tropics, subtropics, and midlatitudes in most models, confirming and expanding on earlier findings. The PET increases are also somewhat sensitive to the time resolution of the inputs, although not as systematically as for the PET climatologies. The changes in the balance between P and PET are also quantified using an alternative aridity index, the ratio , which has a one-to-one but nonlinear correspondence with . It is argued that the magnitudes of changes are more uniformly relevant than the magnitudes of changes, which tend to be much higher in wetter regions. The ratio and its changes are also found to be excellent statistical predictors of the land surface evaporative fraction and its changes.

scholarly journals Projection of Droughts in Amu Darya River Basin for Shared Socioeconomic Pathways

2021 ◽  
Author(s):  
Obaidullah Salehie ◽  
Mohammed Magdy Hamed ◽  
Tarmizi bin Ismail ◽  
Shamsuddin Shahid
Keyword(s):  
River Basin ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Multimodel Ensemble ◽  
Amu Darya ◽  
Near Future ◽  
Far Future ◽  
Amu Darya River

Abstract Droughts significantly affect socioeconomic and the environment primarily by decreasing the water availability of a region. This study aims to assess the changes in drought characteristics in Central Asia's transboundary Amu Darya river basin for four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The precipitation, maximum and minimum temperature (Pr, Tmx and Tmn) simulations of 19 global climate models (GCMs) of the coupled model intercomparison project phase 6 (CMIP6) were used to select the best models to prepare the multimodel ensemble (MME). The standard precipitation evapotranspiration index (SPEI) was used to estimate droughts for multiple timescales from Pr and potential evapotranspiration (PET) derived from Tmx and Tmn. The changes in the frequency and spatial distribution of droughts for different severities and timescales were evaluated for the two future periods, 2020–2059 and 2060-2099, compared to the base period of 1975-2014. The study revealed four GCMs, AWI-CM-1-1-MR, CMCC-ESM2, INM-CM4-8 and MPI-ESM1-2-LR, as most suitable for projections of droughts in the study area. The multimodel ensemble (MME) mean of the selected GCMs showed a decrease in Pr by -3 to 12% in the near future and a change in the range of 3 to -9% in the far future in most parts of the basin for different SSPs. The PET showed almost no change in most parts of the basin in the near future and an increase in the range of 10 to 70% in the far future. The change (%) in projected drought occurrence showed to noticeably decrease in the near future, particularly for moderate droughts by up to ≤-50% for SSP5-8.5 and an increase in the far future by up to ≥30% for SSP3-7.0. The increase in all severities of droughts was projected mostly in the center and northwest of the basin. Overall, the results showed a drought shift from the east to the northwest of the basin in the future.

Relative Contribution of Anthropogenic Forcing and Natural Processes to Rainfall Variability over Victoria, Australia

2020 ◽  
Author(s):  
Surendra Rauniyar ◽  
Scott Power
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Models ◽  
Interquartile Range ◽  
Annual Rainfall ◽  
External Forcing ◽  
Cool Season ◽  
Millennium Drought ◽  
Forced Drying

<p>Victoria is the second-most populated and most densely populated state in Australia with a population of over 6.5 million. Over two thirds of the population live in greater Melbourne. It is also a major area for agriculture and tourism and is the second largest economy in Australia, accounting for a quarter of Australia's Gross Domestic Product. Any changes in Victoria's climate has huge impacts in these sectors. Rainfall over Victoria during the cool season (e.g. April to October) has been unusually low since the beginning of the Millennium Drought in 1997 (~12% below the 20<sup>th</sup> century average). Cool season rainfall contributes two-third to annual rainfall and is very important for many crops and for replenishing reservoirs across the state. Here we examine the extent to which this reduction in cool season rainfall is driven by external forcing, and the prospects for future multi-decadal rainfall, taking both external forcing and internal natural climate variability into account.</p><p>We analyse simulations from 40 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under preindustrial and historical forcing, as well as three scenarios for the 21<sup>st</sup> century: Representative Concentration Pathway (RCP)2.6, RCP4.5 and RCP8.5, which vary markedly in the amount of greenhouse gas emitted over the coming century. While the 1997-2018 average rainfall for cool season is below the preindustrial average in more than two-thirds of models under the three scenarios, the magnitude of the externally-forced drying is very small (median decline is around -2.5% in all three scenarios with an interquartile range around -5% to +1%). The model ensemble results suggest that external forcing contributed only 20% (interquartile range -41% to 4%) to the drying observed in 1997-2018, relative to 1900-1959. These results suggest that the observed drying was dominated by natural, internal rainfall variability. While the multi-model median is below average from 1997-2018 onwards, the externally-forced drying only becomes clear from 2010-2029, when the proportion of models exhibiting drying increases to over 90% under all three scenarios. This agreement reflects the increase in the magnitude of the externally-forced drying. We estimate that there is a 12% chance that internal rainfall variability will completely offset the externally-forced drying averaged over 2018-2037, regardless of scenario. By the late 21<sup>st</sup> century the externally forced change under RCP8.5 is so large that drying – even after taking internally variability into account - appears inevitable. </p><p>Confidence in the modelled projections is lowered because models have difficulty in simulating the magnitude of the observed decline in rainfall. Some of this difficulty appears to arise because most models seem to underestimate multidecadal rainfall variability. Other candidates are: the observed drying may have been primarily due to the occurrence of an extreme, internally-driven event; the models underestimate the magnitude of the externally-forced drying in recent decades; or some combination of the two. If externally-forced drying is underestimated because the response to greenhouse gases is underestimated then the magnitude of projected changes might also be underestimated.</p>

scholarly journals Increased Drought Risk in South Asia under Warming Climate: Implications of Uncertainty in Potential Evapotranspiration Estimates

2020 ◽  
Vol 21 (12) ◽  
pp. 2979-2996 ◽  
Author(s):  
Saran Aadhar ◽  
Vimal Mishra
Keyword(s):  
Soil Moisture ◽  
South Asia ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Drought Risk ◽  
Severe Drought ◽  
Considerable Uncertainty ◽  
Warming Climate

AbstractObserved and projected changes in potential evapotranspiration (PET) and drought are not well constrained in South Asia. Using five PET estimates [Thornthwaite (PET-TH), Hargreaves–Samani (PET-HS), Penman–Monteith (PET-PM), modified Penman–Monteith (PET-MPM), and energy (PET-EN)] for the observed (1979–2018, from ERA5) and future warming climate, we show that significant warming has occurred in South Asia during 1979–2018. PET changes show considerable uncertainty depending on the method used. For instance, PET-TH has increased significantly while all the other four methods show a decline in PET in the majority of South Asia during the observed period of 1979–2018. The increase in PET-TH is substantially higher than PET-HS, PET-PM, and PET-MPM due to a higher (3–4 times) sensitivity of PET-TH to warming during the observed period. Under the 1.5°, 2.0°, and 2.5°C warming worlds, global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5 GCMs) project increases in PET and drought frequency over the majority of the regions. Drought estimates based on PET-EN and PET-MPM are consistent with soil moisture–based drought estimates and project a substantial increase in the frequency of severe droughts under warming climate in South Asia. In addition, the projected frequency of severe drought based on PET-TH, which is an outlier, is about 5 times higher than PET-EN and PET-MPM. Methods to estimate PET contribute the most in the overall uncertainty of PET and drought projections in South Asia, primarily due to PET-TH. Drought estimates based on PET-TH are not reliable for the observed and projected future climate. Therefore, future drought projections should be either based on PET-EN/PET-MPM or soil moisture.

scholarly journals Historical and future trends in wetting and drying in 291 catchments across China

2017 ◽  
Vol 21 (4) ◽  
pp. 2233-2248 ◽  
Author(s):  
Zhongwang Chen ◽  
Huimin Lei ◽  
Hanbo Yang ◽  
Dawen Yang ◽  
Yongqiang Cao
Keyword(s):  
Climate Changes ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Future Trends ◽  
Wetting And Drying ◽  
Intercomparison Project ◽  
Streamflow Data

Abstract. An increasingly uneven distribution of hydrometeorological factors related to climate change has been detected by global climate models (GCMs) in which the pattern of changes in water availability is commonly described by the phrase dry gets drier, wet gets wetter (DDWW). However, the DDWW pattern is dominated by oceanic areas; recent studies based on both observed and modelled data have failed to verify the DDWW pattern on land. This study confirms the existence of a new DDWW pattern in China after analysing the observed streamflow data from 291 Chinese catchments from 1956 to 2000, which reveal that the distribution of water resources has become increasingly uneven since the 1950s. This pattern can be more accurately described as drier regions are more likely to become drier, whereas wetter regions are more likely to become wetter. Based on a framework derived from the Budyko hypothesis, this study estimates runoff trends via observations of precipitation (P) and potential evapotranspiration (Ep) and predicts the future trends from 2001 to 2050 according to the projections of five GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under three scenarios: RCP2.6, RCP4.5, and RCP8.5. The results show that this framework has a good performance for estimating runoff trends; such changes in P play the most significant role. Most areas of China, including more than 60 % of catchments, will experience water resource shortages under the projected climate changes. Despite the differences among the predicted results of the different models, the DDWW pattern does not hold in the projections regardless of the model used. Nevertheless, this conclusion remains tentative owing to the large uncertainties in the GCM outputs.

scholarly journals A GCMs-based mathematic model for droughts prediction in the Haihe Basin, China: Multi-GCM Divide-Integration

2016 ◽  
Author(s):  
Dongmei Han ◽  
Denghua Yan ◽  
Xinyi Xu ◽  
Zhongwen Yang ◽  
Yajing Lu
Keyword(s):  
Water Resources ◽  
Spatial Scale ◽  
Climate Models ◽  
Global Climate ◽  
Mathematic Model ◽  
Global Climate Models ◽  
Annual Rainfall ◽  
Severe Drought ◽  
Data Sets ◽  
Integration Model

Abstract. Recently, the skilful prediction of climate change has drawn high attention from the scientific community. Evidence has been reported the skill of prediction is not satisfactory for the magnitude of inter-annual precipitation and extreme precipitation, and at a smaller spatial scale as well. Based on observational data sets and outputs from the Global Climate Models (GCMs), this study aims at achieving a mathematical model, named multi-GCM divide-integration model (MGDI). The MGDI model is developed by hybridizing finer spatial scale and multi-linear regression model (MLRM) on five state-of-art of GCMs to improve the skills of five GCMs, which is applied to the second level of water resources regionalization in China. It is found that the performance after MGDI model correction has been improved significantly over that of individual GCMs. The errors between observation and simulation after correction (1.6 % ~ 4.4%) are within the margin of error (smaller than 5 %) and all of the varying trends in each second level of water resources regionalization were same. Furthermore, this study also used the MGDI model to predict the variation of precipitation and droughts at different spatial scale, including second level of water resources regionalization of China and the whole HHB, for the next 40 years. Predictions indicate the climate will gradually change from drying to wetting over the HHB wherein the trend of annual rainfall is 9.3 mm/10 a. The frequency of drought events will be decreasing as time goes on. The occurrence of mild and severe drought in the Luan River and Jidong Coastal, Tuhai majia River are higher than that in other regions, 9 and 8 respectively. These findings would provide scientific support for current water resources management and future drought-resisting planning of districts in China.

scholarly journals Projected Rainfall Erosivity Over Central Asia Based on CMIP5 Climate Models

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 897 ◽  
Author(s):  
Duulatov ◽  
Chen ◽  
Amanambu ◽  
Ochege ◽  
Orozbaev ◽  
...  
Keyword(s):  
Central Asia ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Delta Method ◽  
Annual Rainfall ◽  
Steady Increase ◽  
Rainfall Erosivity ◽  
Cmip5 Climate Models ◽  
Projected Changes

Climate change-induced precipitation variability is the leading cause of rainfall erosivity that leads to excessive soil losses in most countries of the world. In this paper, four global climate models (GCMs) were used to characterize the spatiotemporal prediction of rainfall erosivity and assess the effect of variations of rainfall erosivity in Central Asia. The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR) were statistically downscaled using the delta method under Representative Concentration Pathways (RCPs) 2.6 and 8.5 for two time periods: “Near” and “Far” future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity and its projected changes over Central Asia. WorldClim data was used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine rainfall erosivity. The results show an increase in the future periods of the annual rainfall erosivity compared to the baseline. For all GCMs, with an average change in rainfall erosivity of about 5.6% (424.49 MJ mm ha−1 h−1 year−1) in 2030s and 9.6% (440.57 MJ mm ha−1 h−1 year−1) in 2070s as compared to the baseline of 402 MJ mm ha−1 h−1 year−1. The magnitude of the change varies with the GCMs, with the largest change being 26.6% (508.85 MJ mm ha−1 h−1 year−1), occurring in the MIROC-5 RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity.

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