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Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure and precipitation on decadal to multi-decadal time scales. We find that the constrained projections show significant skill in predicting the climate of the following ten to twenty years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first two decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.

Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.

The Arctic atmosphere is warming rapidly and its relatively pristine environment is sensitive to the long-range transport of atmospheric pollutants. While carbon dioxide is the main cause for global warming, short-lived climate forcers (SLCFs) such as methane, ozone, and particles also play a role in Arctic climate on near-term time scales. Atmospheric modelling is critical for understanding the abundance and distribution of SLCFs throughout the Arctic atmosphere, and is used as a tool towards determining SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models, assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over four years (2008–2009 and 2014–2015) conducted for the 2021 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship and aircraft-based observations. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean was able to represent the general features of SLCFs in the Arctic, though vertical mixing, long-range transport, deposition, and wildfire emissions remain highly uncertain processes. These need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment.

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM2.5 (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM2.5 estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM2.5 concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM2.5 observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM2.5 concentration greater than 80 μg/m3 in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM2.5 were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM2.5 estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.

Observations facilitate model evaluation and provide constraints that are relevant to future predictions and projections. Constraints for uninitialized projections are generally based on model performance in simulating climatology and climate change. For initialized predictions, skill scores over the hindcast period provide insight into the relative performance of models, and the value of initialization as compared to projections. Predictions and projections combined can, in principle, provide seamless decadal to multi-decadal climate information. For that, though, the role of observations in skill estimates and constraints needs to be understood in order to use both consistently across the prediction and projection time horizons. This paper discusses the challenges in doing so, illustrated by examples of state-of-the-art methods for predicting and projecting changes in European climate. It discusses constraints across prediction and projection methods, their interpretation, and the metrics that drive them such as process accuracy, accurate trends or high signal-to-noise ratio. We also discuss the potential to combine constraints to arrive at more reliable climate prediction systems from years to decades. To illustrate constraints on projections, we discuss their use in the UK's climate prediction system UKCP18, the case of model performance weights obtained from the Climate model Weighting by Independence and Performance (ClimWIP) method, and the estimated magnitude of the forced signal in observations from detection and attribution. For initialized predictions, skill scores are used to evaluate which models perform well, what might contribute to this performance, and how skill may vary over time. Skill estimates also vary with different phases of climate variability and climatic conditions, and are influenced by the presence of external forcing. This complicates the systematic use of observational constraints. Furthermore, we illustrate that sub-selecting simulations from large ensembles based on reproduction of the observed evolution of climate variations is a good testbed for combining projections and predictions. Finally, the methods described in this paper potentially add value to projections and predictions for users, but must be used with caution. All authors were supported by the EUCP project funded by the European Commission's Horizon 2020 programme, Grant Agreement number 776613. JM was also supported by the french ANR MOPGA project ARCHANGE and by the EU-H2020 Blue Action (GA 727852) and 4C projects (GA 821003). MGD also received funding by the Spanish Ministry for the Economy, Industry and Competitiveness grant reference RYC-2017-22964. Peer Reviewed "Article signat per 14 autors/es: Gabriele C. Hegerl, Andrew P. Ballinger, Ben B. B. Booth, Leonard F. Borchert, Lukas Brunner, Markus G. Donat, Francisco J. Doblas-Reyes, Glen R. Harris, Jason Lowe, Rashed Mahmood, Juliette Mignot, James M. Murphy, Didier Swingedouw and Antje Weisheimer"

Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

In order to study the future aerosol burdens and their radiative and climate impacts over the Arctic (>60 °N), future (2015-2050) simulations have been carried out using the GISS-E2.1 Earth system model. Different future anthrpogenic emission projections have been used from the Eclipse V6b and the Coupled Model Intercomparison Project Phase 6 (CMIP6) databases. Results showed that Arctic BC, OC and SO42- burdens decrease significantly in all simulations following the emission projections, with the CMIP6 ensemble showing larger reductions in Arctic aerosol burdens compared to the Eclipse ensemble. For the 2030-2050 period, both the Eclipse Current Legislation (CLE) and the Maximum Feasible Reduction (MFR) ensembles simulated an aerosol top of the atmosphere (TOA) forcing of -0.39±0.01 W m-2, of which -0.24±0.01 W m-2 were attributed to the anthropogenic aerosols. The CMIP6 SSP3-7.0 scenario simulated a TOA aerosol forcing of -0.35 W m-2 for the same period, while SSP1-2.6 and SSP2-4.5 scenarios simulated a slightly more negative TOA forcing (-0.40 W m-2), of which the anthropogenic aerosols accounted for -0.26 W m-2. The 2030-2050 mean surface air temperatures are projected to increase by 2.1 °C and 2.4 °C compared to the 1990-2010 mean temperature according to the Eclipse CLE and MFR ensembles, respectively, while the CMIP6 simulation calculated an increase of 1.9 °C (SSP1-2.6) to 2.2 °C (SSP3-7.0). Overall, results show that even the scenarios with largest emission reductions lead to similar impact on the future Arctic surface air temperatures compared to scenarios with smaller emission reductions, while scenarios with no or little mitigation leads to much larger sea-ice loss, implying that even though the magnitude of aerosol reductions lead to similar responses in surface air temperatures, high mitigation of aerosols are still necessary to limit sea-ice loss.

Model initialization is a matter of transferring the observed information available at the start of a forecast to the model. An optimal initialization is generally recognized to be able to improve climate predictions up to a few years ahead. However, systematic errors in models make the initialization process challenging. When the observed information is transferred to the model at the initialization time, the discrepancy between the observed and model mean climate causes the drift of the prediction toward the model-biased attractor. Although such drifts can be generally accounted for with a posteriori bias correction techniques, the bias evolving along the prediction might affect the variability that we aim at predicting, and disentangling the small magnitude of the climate signal from the initial drift to be removed represents a challenge. In this study, we present an innovative initialization technique that aims at reducing the initial drift by performing a quantile matching between the observed state at the initialization time and the model state distribution. The adjusted initial state belongs to the model attractor and the observed variability amplitude is scaled toward the model one. Multi-annual climate predictions integrated for 5 years and run with the EC-Earth3 Global Coupled Model have been initialized with this novel methodology, and their prediction skill has been compared with the non-initialized historical simulations from CMIP6 and with the same decadal prediction system but based on full-field initialization. We perform a skill assessment of the surface temperature, the heat content in the ocean upper layers, the sea level pressure, and the barotropic ocean circulation. The added value of the quantile matching initialization is shown in the North Atlantic subpolar region and over the North Pacific surface temperature as well as for the ocean heat content up to 5 years. Improvements are also found in the predictive skill of the Atlantic Meridional Overturning Circulation and the barotropic stream function in the Labrador Sea throughout the 5 forecast years when compared to the full field method. This study was supported by the project LISTEN funded by the European Commission Horizon 2020 Marie Skłodowska-Curie Actions - IF (GA 799930). The authors thankfully acknowledge the computer resources from the ECMWF special project INCIPIT (spitvolp) and the technical assistance provided by ECMWF and BSC. The climate simulations have been performed using Autosubmit workflow manager (Manubens-Gil et al., 2016). The authors thank Paolo Davini for providing the restart files of the historical simulation used to implement the quantile matching. We acknowledge Saskia Loosveldt and the earthdiags and ESMValTool suite developers, as well as the startR and s2dverification (Manubens et al., 2018) software packages developers, as these tools were used to postprocess, analyze, and visualize the results presented in this work. PO acknowledges support by the Spanish Ministry of Economy, Industry and Competitiveness through the Ramon y Cajal grant (RYC-2017-22772).

Various drought indices are normally used to monitor drought and its risk management. Precipitation, temperature and other hydro meteorological parameters are the essential parts to the identification of drought. For this purpose, several drought indices have been developed and are being used around the world. This study identifies the applicability and comparison of drought indices in Pakistan by evaluating the performance of 15 drought indices. The indices include standardized precipitation index (SPI), standardized precipitation temperature index, standardized precipitation evapotranspiration index (SPEI), China Z-Index, deciles index, modified CZI, Z-Score, rainfall variability index, standardized soil moisture anomaly index, weighted anomaly standardized precipitation index, percent of normal precipitation index, self-calibrated Palmer drought severity index, composite index, percentage area weighted departure and reconnaissance drought index (RDI). These indices are compared by utilizing long term data of 58 meteorological stations for the period 1951–2014. The performance, efficiency and significance are also tested by applying different statistical tests. The SPI, SPEI and RDI results showed a good capability to monitor drought status in Pakistan. The positive increasing trend (towards wetness) is noted by several of the aforementioned indices at 95% confidence level. In addition, historical drought years and intensity have been explored along with comparison of recent long episode of drought (1999–2002) and all the indices captured this period successfully.

Considering the importance of black carbon (BC), this study began by comparing the 20th century simulation of South Asian summer climate in IPCC CMIP3, based on the scenario of models with and without BC. Generally, the multi-model mean of the models that include BC reproduced the observed climate relatively better than those that did not. Then, the 21st century South Asian summer precipitation was projected based on the IPCC CMIP3 projection simulations. The projected precipitation in the present approach exhibited a considerable difference from the multimodel ensemble mean (MME) of IPCC AR4 projection simulations, and also from the MME of the models that ignore the effect of BC. In particular, the present projection exhibited a dry anomaly over the central Indian Peninsula, sandwiched between wet conditions on the southern and northern sides of Pakistan and India, rather than homogeneous wet conditions as seen in the MME of IPCC AR4. Thus, the spatial pattern of South Asian summer rainfall in the future may be more complicated than previously thought.

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM

Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol-climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is of great importance. The objective of this study is to evaluate nine operational global aerosol optical depth (AOD) products, including the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging Spectro Radiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) over the land and oceans during the period from 1997 to 2017. Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud screened and quality assured) measurements at 243 sites around the world are selected for validation purposes. Our results illustrate that noticeable inconsistencies exist in spatial coverages, variations, and temporal trends among different aerosol products. Large annual mean AOD differences greater than 0.15 are mainly found in North Africa, Central Africa, the Middle East, and East Asia, as well as in the coastal areas of western North America and Asia. In general, the AATSR Swansea University (SU) product appears to have the best accuracy, the SeaWiFS product obviously underestimates aerosol loadings over the land and ocean, and the MODIS products seriously overestimate aerosol loadings over the land and ocean. For temporal trends, more than 77% and 72% of land and ocean sites, respectively, show the same trends direction as the ground observations. However, numerical differences are non-negligible in North Africa, the Middle East, and South Asia and in most open oceans. MODIS products are generally more accurate in describing the temporal trends over the land and ocean. The aerosol loadings trend changed from positive to negative over the past two decades (2000−2017) in some continental areas. These results suggest that multiple AOD products show significant numerical inconsistencies in describing spatial distributions and temporal trends, which should be carefully considered when users choose aerosol products for their studies.

Motivated by the need to predict how the Arctic atmosphere will change in a warming world, this article summarizes recent advances made by the research consortium NETCARE (Network on Climate and Aerosols: Addressing Key Uncertainties in Remote Canadian Environments) that contribute to our fundamental understanding of Arctic aerosol particles as they relate to climate forcing. The overall goal of NETCARE research has been to use an interdisciplinary approach encompassing extensive field observations and a range of chemical transport, earth system, and biogeochemical models. Several major findings and advances have emerged from NETCARE since its formation in 2013 . (1) Unexpectedly high summertime dimethyl sulfide (DMS) levels were identified in ocean water and the overlying atmosphere in the Canadian Arctic Archipelago (CAA). Furthermore, melt ponds, which are widely prevalent, were identified as an important DMS source. (2) Evidence was found of widespread particle nucleation and growth in the marine boundary layer in the CAA in the summertime. DMS-oxidation-driven nucleation is facilitated by the presence of atmospheric ammonia arising from sea bird colony emissions, and potentially also from coastal regions, tundra, and biomass burning. Via accumulation of secondary organic material (SOA), a significant fraction of the new particles grow to sizes that are active in cloud droplet formation. Although the gaseous precursors to Arctic marine SOA remain poorly defined, the measured levels of common continental SOA precursors (isoprene and monoterpenes) were low, whereas elevated mixing ratios of oxygenated volatile organic compounds were inferred to arise via processes involving the sea surface microlayer. (3) The variability in the vertical distribution of black carbon (BC) under both springtime Arctic haze and more pristine summertime aerosol conditions was observed. Measured particle size distributions and mixing states were used to constrain, for the first time, calculations of aerosol–climate interactions under Arctic conditions. Aircraft- and ground-based measurements were used to better establish the BC source regions that supply the Arctic via long-range transport mechanisms. (4) Measurements of ice nucleating particles (INPs) in the Arctic indicate that a major source of these particles is mineral dust, likely derived from local sources in the summer and long-range transport in the spring. In addition, INPs are abundant in the sea surface microlayer in the Arctic, and possibly play a role in ice nucleation in the atmosphere when mineral dust concentrations are low. (5) Amongst multiple aerosol components, BC was observed to have the smallest effective deposition velocities to high Arctic snow.

This study quantifies black carbon (BC) processes in three global climate models and one chemistry transport model, with focus on the seasonality of BC transport, emissions, wet and dry deposition in the Arctic. In the models, transport of BC to the Arctic from lower latitudes is the major BC source for this region. Arctic emissions are very small. All models simulated a similar annual cycle of BC transport from lower latitudes to the Arctic, with maximum transport occurring in July. Substantial differences were found in simulated BC burdens and vertical distributions, with Canadian Atmospheric Global Climate Model (CanAM) (Norwegian Earth System Model, NorESM) producing the strongest (weakest) seasonal cycle. CanAM also has the shortest annual mean residence time for BC in the Arctic followed by Swedish Meteorological and Hydrological Institute Multiscale Atmospheric Transport and Chemistry model, Community Earth System Model, and NorESM. Overall, considerable differences in wet deposition efficiencies in the models exist and are a leading cause of differences in simulated BC burdens. Results from model sensitivity experiments indicate that convective scavenging outside the Arctic reduces the mean altitude of BC residing in the Arctic, making it more susceptible to scavenging by stratiform (layer) clouds in the Arctic. Consequently, scavenging of BC in convective clouds outside the Arctic acts to substantially increase the overall efficiency of BC wet deposition in the Arctic, which leads to low BC burdens and a more pronounced seasonal cycle compared to simulations without convective BC scavenging. In contrast, the simulated seasonality of BC concentrations in the upper troposphere is only weakly influenced by wet deposition in stratiform clouds, whereas lower tropospheric concentrations are highly sensitive.

The remote impact of South Asian black carbon (SABC) aerosol on East Asian summer monsoon (EASM) was studied and compared with the local impact of local East Asian black carbon (EABC) by using ensemble sensitive experiments in atmospheric general circulation model, GFDL AM2.1. The results show that SABC causes a south–north tripolar precipitation response pattern over East Asia. This includes reduced rainfall over a central area from the Yangtze River valley to East China Sea and southern Japan but intensified rainfall over a northern domain ranging from northern China/Korea to northern Japan as well as over a southern domain, southern China. Such SABC induced changes are about 5–10% of the observed climatological rainfalls in East Asia. Mechanistically, such a remote effect is realized through a propagating wave train along the Asian upper tropospheric jet. The wave train disperses South Asian atmospheric signals downstream and causes intensification of the western Pacific subtropical anticyclone (WPSA), resulting in weakened moisture supply toward East Asia from the Bay of Bengal. In addition, the remote circulation response enhances atmospheric stability over the Yangtze River valley. These two effects together cause rainfall reduction in the Yangtze River valley. Besides, the remote effect tends to offset a fraction of rainfall intensification induced by local EABC. Copyright © 2013 Royal Meteorological Society

The influence of local black carbon (BC) on monthly mean precipitation and the associated circulation in South Asia is investigated. The results show that the amplified shortwave radiative heating rate by BC absorption enhances convective activity near the Himalayas and the southern Tibetan Plateau with increased rainfall anomalies in April and May, which is consistent with previous studies. However, the enhanced vertical motions and the precipitation ultimately lead towards cooling of the lower troposphere especially in May, which is extended till June. This favored negative rainfall responses in June, implying delay in the onset of South Asian summer monsoon rainfalls over Arabian Sea and western parts of the subcontinent. The negative precipitation response is further associated with the prevailing anomalous high pressure and the anticyclonic wind circulations induced by BC. Thus, we present here one different feature associated with BC-induced elevated heat pump-like circulations in South Asia.

Two inter-decadal shifts in East China summer rainfall during the last three decades of the 20th century have been identified. One shift occurred in the late 1970s and featured more rainfall in the Yangtze River valley and prolonged drought in North China. The other shift occurred in the early 1990s and featured increased rainfall in South China. The role of black carbon (BC) aerosol in the first shift event is controversial, and it has not been documented for the second event. In this study, the authors used Geophysical Fluid Dynamics Laboratory’s (GFDL’s) atmospheric general circulation model known as Atmosphere and Land Model (AM2.1), which has been shown to capture East Asian climate variability well, to investigate these issues by conducting sensitive experiments with or without historical BC in East Asia. The results suggest that the model reproduces the first shift well, including intensified rainfall in the Yangtze River and weakened monsoonal circulation. However, the model captures only ...

Water has always played a vital role in human societies. In order to manage the water resources, many countries have built water reservoirs, for example, dams for sustainable development, growth and poverty reduction. Vegetation, in the watershed region of a dam, plays a very vital role on soil erosion and consequently on the sediments deposition in the water reservoir. This study intends to analyze the impact of vegetation cover on soil erosion and thereby the sediments deposition in the watershed region of a dam by using satellite remote sensing technique. For this, Mangla dam which is one of the most important water reservoirs in Pakistan and its capacity has been reduced by more than 20% since its construction, is selected as the study area. Shuttle Radar Topographic Mission (SRTM) data, onboard the space shuttle Endeavor, was used to calculate the total drainage area for the Mangla watershed. Landsat images for the years 1979, 1980, 1992, 1998 and 2002 were used to estimate the total area of vegetation in the Mangla watershed region. In order to estimate the vegetation area, two vegetation indices, that is, Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were applied on Landsat MSS, TM, and ETM+ images. The comparison of sedimentation and vegetation data indicates that except for the year 1992, the load of sedimentation in Mangla dam decreases with an increase in the vegetation area in the Mangla watershed region. The maximum vegetation in the Mangla watershed region is estimated in the year 2002, and subsequently, the load of sediments in the same year in Mangla dam is minimum. The current study thus indicates that the soil erosion which is the main reason of sedimentation in water reservoir can be controlled with the help of plantation of various species of vegetation in the watershed region of the reservoir.

South Asian monsoons were analyzed within the context of increasing emissions of black carbon (BC) aerosols using a global atmospheric general circulation model. The BC aerosols were allowed to inc...

Soil erosion is the most important reason of sedimentation load of water reservoirs in the world. In Pakistan, Mangla dam is one of the most important water reservoirs used for the production of electricity and for the supply of water for irrigation purposes. However, the capacity of Mangla dam reservoir has reduced by more than 20% since its construction. This study highlights the impact of rainfall on soil erosion and consequently on sedimentation deposition in Mangla dam reservoir. Sedimentation, annual rainfall, and normal rainfall data of 39 years were used in this study. Shuttle Radar Topographic Mission data were used to calculate the total drainage area of the Mangla watershed region. The sedimentation data of Mangla reservoir from 1967 to 2005 were retrieved from Water and Power Development Authority in Pakistan. The meteorological observatories in the Mangla watershed region are identified. Annual rainfall data from 1967 to 2005 for the meteorological observatories in the Mangla watershed regions were retrieved from Pakistan Meteorological Department (PMD). In addition, normal rainfall data for the years 1949 to 1978 and for the years 1979 to 2008 were also retrieved from PMD. The impact of annual rainfall is observed on sedimentation load in Mangla dam. The correlation coefficient between annual rainfall and sedimentation load is 0.94. This study shows that with an increase in rainfall, the soil erosion of the area increases which subsequently is responsible for the increase in the rate of sedimentation load in Mangla dam. This study further demonstrates that better soil management can reduce the sedimentation load in the Mangla reservoir.

In view of the growing needs of energy in Pakistan, the efficient use and development of renewable energy sources has become a major issue in the country. This has brought the intention of several national and multinational companies to design and implement a major work plan for energy conservation and construction of renewable energy sources like wind mills and solar panels. Fortunately, Pakistan is among those countries in which sun warms the surface throughout the year and therefore has a strong potential for solar power generation. This study was conducted to explore those areas which are most suitable for solar energy potential using fifty eight meteorological stations covering the whole country. Angstrom equation and Hargreaves formula was used to calculate monthly solar energy potential by utilizing monthly climatical data of bright sunshine hours, mean maximum and minimum temperatures. The lowest solar radiation intensity 76.49 W/m2 observed at Cherat during December and highest 339.25 W/m2 at Gilgit. The average monthly solar radiation intensity remains 136.05 to 287.36 W/m2 in the country. The results indicate that the values of solar radiation intensity greater than 200 W/m2 were observed in the months: February to October in Sindh, March to October in almost all regions of Balochistan, April to September in NWFP, Northern Areas and Kashmir regions while March to October in Punjab. For 10 h a day, average solar radiation intensity ranges from 1500 W/m2/day to 2750 W/m2/day in Pakistan especially in southern Punjab, Sindh and Balochistan regions throughout the year. In an area of 100 m2, 45 MW to 83 MW power per month may be generated in the above mentioned regions.