npj Climate and Atmospheric Science最新文献

Global warming is jeopardizing the artificial snow making conditions, shortening outdoor ski resorts opening days and increasing operation cost, severely threatening the sustainability of outdoor ski industry. The sustainability of 772 outdoor ski resorts in China under RCP 4.5 climate scenarios in 2030 s and 2050 s had been analyzed. (1) The skiing sports developed prominently during 1996 to 2023 and will boom in China in the coming decades. (2) Accelerated global warming is the main threat to the sustainability of outdoor ski resorts of China. However, snowfall isn’t a critical influencing factor in the coming decades. (3) The outdoor ski resorts in south China are facing the most threats and are the most unsustainable resorts. We proposed a nexus of Government-Operator-Skier adaptation framework for adapting climate change threats and advocating the temporal small-scale ski resorts are more adaptive as their high water and energy efficiencies for saving water and electricity resources.

The global flood risk urges an improved understanding of flood magnitude and its mechanism, which needs insights from pre-instrumental flood investigations. Due to data scarcity, reconstructing pre-instrumental flood magnitudes relies on statistical downscaling, failing to capture nonlinear and dynamic characteristics. We developed a dynamical approach, NorESM-WRF-SWAT, integrating a global climate, a regional, and a hydrologic model to investigate the 1931 Yangtze River flood (the deadliest in the world) and compared it with the 1998’s. Through validation, our method outperforms the statistical method in simulating precipitations and river discharges. For the first time, we presented detailed insights into the intensity and duration of the 1931 flood, revealing a smaller magnitude but associated with an amplified loss, likely due to social vulnerability and reduced societal resilience compared to the 1998’s. While successful simulation can be interfered with by model variability, our dynamical method shows promise for simulating pre-instrumental flood and building a long-term pre-instrumental-hydrology database.

Simulating accurately the South Asian summer monsoon is crucial for food security of several South Asian countries yet challenging for global climate models (GCMs). The GCMs suffer from some systematic biases including dry bias in mean monsoon rainfall over the India subcontinent and excessive equatorial light rain between which the relationship was rarely discussed. Numerical experiments are conducted for one month during active monsoon with global quasi-uniform resolution of 60 km (U60 km) and 3 km (U3 km) separately. Evaluation with observations shows that U3 km reduces the dry bias over northern India and excessive light rain over the equatorial Indian Ocean (EIO) that are both prominent in U60 km. Excessive light rain in U60 km contributes critically to stronger rainfall and latent heating over the EIO. A Hadley-type anomalous circulation is thus induced, whose subsidence branch suppresses updrafts and reduces moisture transport into northern India, contributing to the dry bias. The findings highlight the importance of constraining excessive light rain for regional climate projection in GCMs.

In this study, we examine the predictability of tropical cyclone (TC) track density in the subseasonal-to-seasonal (S2S) reforecast ensembles of the European Centre for Medium-range Weather Forecasts (ECMWF) using the method of average predictability time (APT). Eleven of the retrieved APT modes (APTMs) of TC track density possess an APT longer than 1 week. The most predictable of them, APTM-1, has an APT of almost three weeks and is found to be closely linked to the Boreal Summer Intraseasonal Oscillation (BSISO) and monsoon variability. Another discovery is the strong relationship between APTM-7 and the activity of mixed Rossby-gravity (MRG) waves and tropical depression (TD) type disturbances despite its short APT of ~12 days. We further carry out a simple case analysis to see how the relatively high predictability of APTM-1 manifests in the S2S model. Our work provides a new possibility for improving medium-range TC forecast skill, and has revealed how underlying tropical variability can play a role in determining TC predictability.

Inaccurate characterization of complex topography leads to the wet bias in climate models, particularly affecting terrain effects in regions like the Tibetan Plateau (TP). This study utilizes the Weather Research and Forecasting (WRF) model with multiple terrain datasets and introduces the terrain complexity index (TCI) to quantify the degree of terrain changes, aiming to evaluate how terrain complexity affects the cloud and precipitation processes over the TP. The results indicate that fine terrain complexity primarily causes earlier cloud formation and precipitation, resulting in more heavy precipitation on the southern slope of the TP (SSTP) and more light precipitation on the TP platform. The structure of moisture transport and microphysical processes further reveals that this promotes the formation of more medium and high clouds, increasing the proportion of solid precipitation over the SSTP. Over the TP platform, the restriction of medium and high cloud development with enhancing the proportion of low clouds for more liquid precipitation. These findings deepen the understanding of the TP’s complex terrain effect on cloud and precipitation changes in the Asian water cycle.

Accurately estimating particulate organic nitrate under high NO_x and oxidizing conditions is critical. This study compared the NO_x⁺ ratio, unconstrained Positive Matrix Factorization (PMF), and Multilinear Engine-2 (ME2) methods to estimate particulate organic nitrate in Shanghai across different seasons. The factors associated with organic nitrate, as identified through two receptor methods, exhibited consistent daily patterns in spring, summer, and autumn, although source contributions varied. The NO_x⁺ ratio method reported higher organic nitrate levels than the PMF and ME2 methods, likely due to the fixed R_ON/R_AN parameter. Seasonal R_ON/R_AN parameters were optimized based on precursor emissions in Shanghai, achieving values of 3.13 in spring, 2.25 in summer, and 1.88 in autumn. This optimization reduced discrepancies in organic nitrate using the NO_x⁺ ratio to 3.2–7.4%. The optimized parameters in this study support the rapid and accurate estimation of organic nitrate during different seasons in urban areas.

This study leverages the Global/Regional Integrated Model system (GRIMs) version 4.0 climate model to examine the mechanisms behind the recent intensification of winter stationary waves over western North America. Prescribed sea surface temperature warming forces a strengthening of westerly winds, amplifying the ridge that characterizes the stationary waves in western North America. The streamfunction budget analysis reveals relative vorticity advection is mainly associated with this process. We further show that ocean warming is the primary driver of changes in westerly winds and stationary waves in the Northern Hemisphere. Sea ice losses exert a considerable effect through a different mechanism, complementing the dominant influence of ocean warming on these atmospheric changes. Our results thus reveal the crucial role tropical oceans play in modulating global warming’s effect on the stationary waves in the Northern Hemisphere and add a more quantitative perspective to the previously reported influence of Arctic amplification.

Air pollution impacts on human health are of serious concern in northern India, and over the Delhi National Capital Region (NCR) in particular. The Kharif crop residue burning (CRB) is often blamed for degradation of Delhi-NCR’s seasonal air quality. However, the concentration of fine particulate matter (PM_2.5) remained stable in Delhi, while the fire detection counts (FDCs) from satellites over Punjab and Haryana declined by 50% or more during 2015–2023. We measured PM_2.5, carbon monoxide (CO) and related parameters over Delhi-NCR, Haryana and Punjab from a network of 30 low-cost sensors (CUPI-Gs) in a selected period (September–November) of 2022 and 2023. Measured PM_2.5 showed lower concentration in 2023 compared to 2022 at Punjab and Haryana sites, in compliance with FDC reductions. Using the CUPI-G measurements, airmass trajectories, particle dispersion and chemical-transport model simulations, we show that the CRB emissions over Punjab contributed only a meagre ~14% to the overall PM_2.5 over Delhi-NCR during October-November 2022. This indicates that there exists only a very weak coupling between PM_2.5 mass over Delhi-NCR and the CRB over Punjab, highlighting the effectiveness of the Graded Response Action Plan (GRAP) in controlling air pollution in the region.

Climate change is considered to affect wildfire spread both by increasing fuel dryness and by altering vegetation mass and structure. However, the direct effect of global warming on wildfires is hard to quantify due to the multiple non-climatic factors involved in their ignition and spread. By combining wildfire observations with the latest generation of climate models, here we show that more than half of the large wildfires (area>500 ha) occurring in the Iberian Peninsula between 2001 and 2021 present a significant increase in the rate of spread with respect to what it would have been in the pre-industrial period, attributable to global warming. The average acceleration of the rate of spread due to increased fuel dryness is between 2.0% and 8.3%, whereas the influence of enhanced vegetation growth since the pre-industrial period could potentially be even higher than the direct impact of temperature increase in fuel conditions.

Under climate change, China faces intensifying compound extreme events with serious socio-economic ramifications, yet their future variations remain poorly understood. Here, we estimate historical hotspots and future changes of two typical compound events, i.e., sequential heatwave and precipitation (SHP) and concurrent drought and heatwave (CDH) across China, leveraging a bivariate bias correction method to adjust projections from global climate models. Results show substantial future increases in frequency, duration, and magnitude for both events, with the durations projected to double nationwide. The increases are more evident under higher emission scenarios, and could be largely underestimated if neglecting variable dependence during bias correction process. The projected changes will escalate socio-economic exposure across China’s major urban clusters, among which Guangdong-Hong Kong-Macao will face the highest risk. Our findings underscore the necessity of carbon emission controls, and call for adaptive measures to mitigate the threats induced by rising compound hazards in a changing climate.