Windows of opportunity for predicting seasonal climate extremes highlighted by the Pakistan floods of 2022

Skilful predictions of near-term climate extremes are key to a resilient society. However, standard methods of analysing seasonal forecasts are not optimised to identify the rarer and most impactful extremes. For example, standard tercile probability maps, used in real-time regional climate outlooks, failed to convey the extreme magnitude of summer 2022 Pakistan rainfall that was, in fact, widely predicted by seasonal forecasts. Here we argue that, in this case, a strong summer La Niña provided a window of opportunity to issue a much more confident forecast for extreme rainfall than average skill estimates would suggest. We explore ways of building forecast confidence via a physical understanding of dynamical mechanisms, perturbation experiments to isolate extreme drivers, and simple empirical relationships. We highlight the need for more detailed routine monitoring of forecasts, with improved tools, to identify regional climate extremes and hence utilise windows of opportunity to issue trustworthy and actionable early warnings.

Population Exposure Changes to Mean and Extreme Climate Events Over Pakistan and Associated Mechanisms

The increasing prevalence of warmer trends and climate extremes exacerbate the population's exposure to urban settlements. This work investigated population exposure changes to mean and extreme climate events in different Agro-Ecological Zones (AEZs) of Pakistan and associated mechanisms (1979-2020). Spatiotemporal trends in mean and extreme temperatures revealed significant warming mainly over northern, northeastern, and southern AEZs. In contrast, mean-to-extreme precipitation changes showed non-uniform patterns with a significant increase in the northeast AEZs. Population exposure to mean (extreme) temperature and precipitation events increased two-fold during 2000-2020. The AEZs in urban settlements (i.e., Indus Delta, Northern Irrigated Plain, and Barani/Rainfall) show a maximum exposure to extreme temperatures of about 70-100 × 106 (person-days) in the reference period (1979-1999), which increases to 140-200 × 106 person-days in the recent period (2000-2020). In addition, the highest exposure to extreme precipitation days also increases to 40-200 × 106 person-days during 2000-2020 than 1979-1999 (20-100 × 106) person-days. Relative changes in exposure are large (60%-90%) for the AEZs across northeast Pakistan, justifying the spatial population patterns over these zones. Overall, the observed changes in exposure are primarily attributed to the climate effect (50%) over most AEZs except Northern Irrigated Plain for R10 and R20 events, where the interaction effect takes the lead. The population exposure rapidly increased over major AEZs of Pakistan, which could be more vulnerable to extreme events due to rapid urbanization and population growth in the near future.

The Influence of Teleconnections on the Precipitation in Baluchistan

Precipitation plays a vital role in the economies of agricultural countries, such as Pakistan. Baluchistan is the largest province in Pakistan (in terms of land) and it is facing reoccurring droughts due to changing precipitation patterns. The landscape of the province consists of rugged terrain, mountains, hills, and valleys. The torrential rains lead to devastating flash floods due to the topography of the province, which has proven to be more catastrophic in nature. It is quite intriguing to observe the changing precipitation patterns in Baluchistan. Precipitation has become less frequent but intense, resulting in flash floods and landslides, as well as damage to agriculture, infrastructure, trade, environment, and the ecosystem. Baluchistan is under a drought warning and is already facing a water crisis. This study was performed on monthly precipitation time series data obtained from the Pakistan Meteorological Department (PMD) for determining trends in precipitation from 41 years of data (1977 to 2017) over 13 selected stations in Baluchistan. Due to the non-linear nature of the precipitation data, a non-parametric Mann–Kendall (MK) test was used to determine the increasing or decreasing trends in precipitation on a monthly basis. Large-scale atmospheric circulation and climate indices that affected precipitation were considered to determine their influence on precipitation. Statistical techniques of the partial Mann–Kendall (PMK) and Pearson correlation were applied to each station to ascertain the influence on precipitation due to climatic indices.

Water Pathways for the Hindu-Kush-Himalaya and an Analysis of Three Flood Events

The climatology of major sources and pathways of moisture for three locales along the Hindu-Kush-Himalayan region are examined, by use of Lagrangian methods applied to the ERA-Interim dataset, over the period from 1980 to 2016 for both summer (JJA) and winter (NDJ) periods. We also investigate the major flooding events of 2010, 2013, and 2017 in Pakistan, Uttarakhand, and Kathmandu, respectively, and analyse a subset of the climatology associated with the 20 most significant rainfall events over each region of interest. A comparison is made between the climatology and extreme events, in the three regions of interest, during the summer monsoon period. For Northern Pakistan and Uttarakhand, the Indus basin plays the largest role in moisture uptake. Moisture is also gathered from Eastern Europe and Russia. Extreme events display an increased influence of sub-tropical weather systems, which manifest themselves through low-level moisture transport; predominantly from the Arabian sea and along the Gangetic plain. In the Kathmandu region, it is found that the major moisture sources come from the Gangetic plain, Arabian Sea, Red Sea, Bay of Bengal, and the Indus basin. In this case, extreme event pathways largely match those of the climatology, although an increased number of parcels originate from the western end of the Gangetic plain. These results provide insights into the rather significant influence of mid-latitudinal weather systems, even during the monsoon season, in defining the climatology of the Hindu-Kush-Himalaya region, as well as how extreme precipitation events in this region represent atypical moisture pathways. We propose a detailed investigation of how such water pathways are represented in climate models for the present climate conditions and in future climate scenarios, as this may be extremely relevant for understanding the impacts of climate change on the cryosphere and hydrosphere of the region.

Quantification of Climate Warming and Crop Management Impacts on Cotton Phenology

Understanding the impact of the warming trend on phenological stages and phases of cotton (Gossypium hirsutum L.) in central and lower Punjab, Pakistan, may assist in optimizing crop management practices to enhance production. This study determined the influence of the thermal trend on cotton phenology from 1980-2015 in 15 selected locations. The results demonstrated that observed phenological stages including sowing (S), emergence (E), anthesis (A) and physiological maturity (M) occurred earlier by, on average, 5.35, 5.08, 2.87 and 1.12 days decade^-1, respectively. Phenological phases, sowing anthesis (S-A), anthesis to maturity (A-M) and sowing to maturity (S-M) were reduced by, on average, 2.45, 1.76 and 4.23 days decade^-1, respectively. Observed sowing, emergence, anthesis and maturity were negatively correlated with air temperature by, on average, -2.03, -1.93, -1.09 and -0.42 days °C^-1, respectively. Observed sowing-anthesis, anthesis to maturity and sowing-maturity were also negatively correlated with temperature by, on average, -0.94, -0.67 and -1.61 days °C^-1, respectively. Applying the cropping system model CSM-CROPGRO-Cotton model using a standard variety in all locations indicated that the model-predicted phenology accelerated more due to warming trends than field-observed phenology. However, 30.21% of the harmful influence of the thermal trend was compensated as a result of introducing new cotton cultivars with higher growing degree day (thermal time) requirements. Therefore, new cotton cultivars which have higher thermal times and are high temperature tolerant should be evolved.