The risk of floods has increased in South Asia due to high vulnerability and exposure. The August 2022 Pakistan flood shows a glimpse of the enormity and devastation that can further rise under the warming climate. The deluge caused by the floods in 2022, which badly hit the country's southern provinces, is incomparable to any recent events in terms of the vast spatial and temporal scale. The flood event is ranked second in human mortality, while this was the top event that displaced about 33 million people in Pakistan. Using observations and climate projections, we examine the causes and implications of the 2022 flood in Pakistan. Multiday (& SIM;15 days) extreme precipitation on wet antecedent soil moisture conditions was the primary driver of the flood in August 2022. The extreme precipitation in August was caused by two atmospheric rivers that passed over southern Pakistan. Streamflow simulations from the multiple hydrological models show that multiday extreme precipitation was the primary driver of floods. Several flood-affected stations experienced anomalously higher flow than the upstream stations. The 2022 Pakistan flood highlights the adaptation challenges South Asia is facing along with the substantial need for climate mitigation to reduce the risk of such events. Plain Language Summary The Pakistan flood of 2022 received a considerable attention. However, the causes and implications of the events have not been examined. Using observations, satellite data, and reanalysis products, we show that the event was caused by multiday extreme rainfall on wet antecedent conditions. The extreme rainfall was associated with the two atmospheric rivers that transported significant moisture from the Arabian Sea. The flood was primarily driven by the extreme precipitation and other factors (glacier-melt) played a secondary role. Extreme precipitation is projected to increase in a warming climate, which highlight the strong need of adaptation and mitigation.

Characteristics of carbonaceous aerosol (CA) and its light absorption properties are limited in Karachi, which is one of the most polluted metropolitan cities in South Asia. This study presents a comprehensive measurement of seasonality of CA compositions and mass absorption cross- (MAC) of elemental carbon (EC) and water-soluble organic carbon (WSOC) in total suspended particles (TSP) collected from February 2015 to March 2017 in the southwest part of Karachi. The average TSP, organic carbon (OC), and EC concentrations were extremely high with values as 391.0 +/- 217.0, 37.2 +/- 28.0, and 8.53 +/- 6.97 mg/m(3), respectively. These components showed clear seasonal variations with high concentrations occurring during fall and winter followed by spring and summer. SO42-, NO3-, K+, and NH4+ showed similar variations with CA, implying the significant influence on atmospheric pollutants from anthropogenic activities. Relatively lower OC/EC ratio (4.20 +/- 2.50) compared with remote regions further indicates fossil fuel combustion as a primary source of CA. Meanwhile, sea salt and soil dust are important contribution sources for TSP. The average MAC of EC (632 nm) and WSOC (365 nm) were 6.56 +/- 2.70 and 0.97 +/- 0.37 m(2)/g, respectively. MACEC is comparable to that in urban areas but lower than that in remote regions, indicating the significant influence of local emissions. MACWSOC showed opposite distribution with EC, further suggesting that OC was significantly affected by local fossil fuel combustion. In addition, dust might be an important factor increasing MACWSOC particularly during spring and summer. (C) 2020 The Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V.

Absorbing aerosols mainly Black Carbon (BC) have potential effects on the hydrological cycle and climate change over the high-altitude regions particularly in South Asia. The BC measurements are sparse in high altitude locations of the world particularly over the Northern regions of Pakistan. This study investigated the diurnal/monthly variations of BC and its climatic impacts during the period of 2016-2017 over four high altitude locations, i.e., Astore, Gilgit, Sost and Skardu located in the Himalaya-Karakorum-Hindukush (HKH) mountain ranges in Northern Pakistan. The Optical Properties of Aerosols and Clouds (OPAC) model was used for the estimation of aerosol optical properties, e.g., Aerosol Optical Depth (AOD), Asymmetry Parameter (AP) and Single Scattering Albedo (SSA) using the BC number density corresponding to the BC mass concentration. Then the model derived optical properties (AOD, AP and SSA), surface reflectance, ozone and water vapor were used in Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model for the calculation of BC aerosol radiative forcing (ARF) at the Top Of Atmosphere (TOA), Surface (SUR) and within the ATMosphere (ATM). The results revealed that the mean monthly BC concentrations were maximum during November (3.05 +/- 0.7 mu g/m(3)) as well as in December (3.05 +/- 0.5 mu g/m(3)) at Gilgit and minimum during August (1.1 +/- 0.3 mu g/m(3)) at Sost. Correspondingly, the diurnal variation of BC concentrations displayed strong fluctuations, with high concentrations in the late night and early morning during November and December for Astore and Gilgit, respectively. Generally, the BC concentrations were maximum/minimum in the morning/evening during May, June, August and September at all locations. The correlation of BC with different meteorological parameters showed that the BC has positive correlation with temperature and wind speed, while negative with relative humidity and rainfall. The HYSPLIT back trajectory analysis revealed that air masses arrived the study locations from both long distance (Turkmenistan, Tajikistan, Uzbekistan, Iran, Afghanistan, India, and China) and local sources. The monthly mean maximum and minimum BC ARF values at SUR (TOA) were found to be 43.7 +/- 3.0 W/m(2) (8.2 +/- 0.2 W/m(2)) and 16.4 +/- 1.0 W/m(2) (1.2 +/- 0.1 W/m(2)), respectively, giving an averaged atmospheric forcing of 35.7 +/- 2.3 W/m(2) and 15.2 +/- 1.9 W/m(2).

Investigating the trends in the major climatic variables over the Upper Indus Basin (UIB) region is difficult for many reasons, including highly complex terrain with heterogeneous spatial precipitation patterns and a scarcity of gauge stations. The Weather Research and Forecasting (WRF) model was applied to simulate the spatiotemporal variability of precipitation and temperature over the Indus Basin from 2000 through 2015 with boundary conditions derived from the Climate Forecast System Reanalysis (CFSR) data. The WRF model was configured with three nested domains (d01-d03) with horizontal resolutions increasing inward from 36 km to 12 km to 4 km horizontal resolution, respectively. These simulations were a continuous run with a spin-up year (i.e., 2000) to equilibrate the soil moisture, snow cover, and temperature at the beginning of the simulation. The simulations were then compared with TRMM and station data for the same time period using root mean squared error (RMSE), percentage bias (PBIAS), mean bias error (MBE), and the Pearson correlation coefficient. The results showed that the precipitation and temperature simulations were largely improved from d01 to d03. However, WRF tended to overestimate precipitation and underestimate temperature in all domains. This study presents high-resolution climatological datasets, which could be useful for the study of climate change and hydrological processes in this data-sparse region.