Search

As a direct consequence of extreme monsoon rainfall throughout the summer 2022 season Pakistan experienced the worst flooding in its history. We employ a probabilistic event attribution methodology as well as a detailed assessment of the dynamics to understand the role of climate change in this event. Many of the available state-of-the-art climate models struggle to simulate these rainfall characteristics. Those that pass our evaluation test generally show a much smaller change in likelihood and intensity of extreme rainfall than the trend we found in the observations. This discrepancy suggests that long-term variability, or processes that our evaluation may not capture, can play an important role, rendering it infeasible to quantify the overall role of human-induced climate change. However, the majority of models and observations we have analysed show that intense rainfall has become heavier as Pakistan has warmed. Some of these models suggest climate change could have increased the rainfall intensity up to 50%. The devastating impacts were also driven by the proximity of human settlements, infrastructure (homes, buildings, bridges), and agricultural land to flood plains, inadequate infrastructure, limited ex-ante risk reduction capacity, an outdated river management system, underlying vulnerabilities driven by high poverty rates and socioeconomic factors (e.g. gender, age, income, and education), and ongoing political and economic instability. Both current conditions and the potential further increase in extreme peaks in rainfall over Pakistan in light of anthropogenic climate change, highlight the urgent need to reduce vulnerability to extreme weather in Pakistan.

We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (2040–2059 and 2080–2099) relative to the reference period (1995–2014) under four Shared Socioeconomic Pathways (SSPs, SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andes—a result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP1–2.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the, This research work was supported by the Center of Excellence for Climate Change Research, King Abdulaziz University, Jeddah, Saudi Arabia, and the National Climate‐Computing Research Center, which is located within the National Center for Computational Sciences at the ORNL and supported under a Strategic Partnership Project, 2316‐T849‐08, between DOE and NOAA. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC05-00OR22725. The authors thank the World Climate Research Program for making the CMIP6 dataset available for global and regional scale climate research. The authors also thank the Earth System Grid Federation (ESGF) for archiving and providing free access to the CMIP6 dataset. PAA was supported by MINCIENCIAS through the grant No. 80740-490-2020. LMA was supported by the National Institute of Science and Technology for Climate Change Phase 2 under CNPq Grant 465501/2014-1 and FAPESP 15/50122-0. MSR was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) and Minas Gerais Research Funding Foundation (FAPEMIG). YSV was supported by the Peruvian PPR068 programme "Reducción de vulnerabilidad y atención de emergencias por desastres". ÁGM was partially supported by the NOAA grant NA18OAR4310275 and by the Columbia World Project “ACToday”, at Columbia University in the City of New York. This work was supported by the National Agency for Scientific and Technological Promotion (ANPCyT) (Grant No. PICTO-UUMM-2019-00004). The data analysis and all computation work carried out in this study have been performed on the Aziz Supercomputer at King Abdulaziz University's High Performance Computing Center, Jeddah, Saudi Arabia., Peer Reviewed, "Article signat per 27 autors/es: Mansour Almazroui, Moetasim Ashfaq, M. Nazrul Islam, Irfan Ur Rashid, Shahzad Kamil, Muhammad Adnan Abid, Enda O’Brien, Muhammad Ismail, Michelle Simões Reboita, Anna A. Sörensson, Paola A. Arias, Lincoln Muniz Alves, Michael K. Tippett, Sajjad Saeed, Rein Haarsma, Francisco J. Doblas-Reyes, Fahad Saeed, Fred Kucharski, Imran Nadeem, Yamina Silva-Vidal, Juan A. Rivera, Muhammad Azhar Ehsan, Daniel Martínez-Castro, Ángel G. Muñoz, Md. Arfan Ali, Erika Coppola & Mouhamadou Bamba Sylla ", Postprint (published version)