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Extreme events
Extreme events
Heatwaves are becoming more intense, frequent, and prolonged due to global warming, posing significant risks to ecosystems and human societies. Despite their profound impact, detailed regional assessments of extreme heat events remain limited, particularly in India. This study addresses the gap by systematically investigating the 2024 extreme heat event in India. We evaluated the performance of various land surface schemes in simulating heat extremes using the Weather Research and Forecasting model and also assessed the accuracy of Global Forecast System (GFS) forecasts. Our analysis reveals a strong co-occurrence of drought and heat stress during the extreme heat event. This combination results in increased fire risk and negative impacts on vegetation productivity in regions affected by both drought and heat stress highlighting the severe consequences of this compound event. We compare different land surface models (RUC, Noah, Noah-MP, Noah-MP with dynamic vegetation, CLM) against India Meteorological Department (IMD) observations. We observe that Noah is optimal for reducing bias and RMSE, while Noah-MP with dynamic vegetation is most accurate for simulating extreme heat, with the highest hit rate and threat score for the 90th percentile threshold. Additionally, GFS maximum temperature forecasts for 1–3 day lead times perform well at short lead times, especially in Southern India but overestimate temperatures in heatwave-prone regions like the Indo-Gangetic Plains. Our findings highlight the importance of enhancing land surface models and forecasting systems to better predict extreme heat events, which is crucial for localized hazard and risk assessments and improving disaster management efficiency.
In recent decades, India has witnessed an increase in the intensity, frequency, and spread of extreme weather events. The widespread increase in extreme precipitation over the Western Coast of India is a matter of great concern. The factors contributing to such devastating extreme precipitation remain unclear due to the variability present in meteorological and oceanic variables and associated large-scale circulations. Using reanalysis and observed datasets, we attempted to identify a combination of dynamic, thermodynamic, and cloud microphysics processes contributing to the anomalous precipitation from August 1 to 10, 2019 against its climatology. Our key findings highlight the crucial role of warm sea surface temperatures (anomaly >1.4°C), outgoing longwave radiation (anomaly <−50 W·m−2), and atmospheric temperature (anomaly over the ocean is >1.6°C) in enhancing the moisture-holding capacity of the atmosphere by almost 10%. This elevated moisture, propelled by intensified low-level winds (anomalies exceeding 4 m·s−1), leads to a shift from ocean to land. Notably, we observe that vertical updrafts (anomalies >−0.4 m·s−1) contribute to increased atmospheric instability and moisture convergence. The presence of an ample amount of cloud hydrometeors, with anomalies surpassing 2.5 × 10−4 kg·kg−1, establishes conditions conducive to sustained intense precipitation. Our findings deepen our understanding of the complex relationships between ocean and atmospheric dynamics, and wind patterns, and emphasize their pivotal influence on regional weather patterns and land surface hydrology.
Carbon and Water Cycle Interaction
Carbon and Water Cycle Interaction
Complex interaction between Carbon and Water Cycle over India
Vegetation acts as a major link between the water and carbon cycles, both of which are essential for sustaining life on Earth. The interactions within these cycles are significantly influenced by climate change, making it essential to understand their coupling.
In recent decades, India has witnessed rapid socio-economic development and population growth, leading to a sharp rise in CO₂ emissions. Interestingly, despite this upward trend in anthropogenic emissions, satellite observations show that India is the second-largest contributor to global greening. This apparent contradiction between increasing emissions and expanding vegetation cover adds complexity to the terrestrial carbon budget.
Balancing economic growth with environmental sustainability is emerging as a pressing challenge. In this context, improving our understanding of how carbon and water cycles interact under India’s evolving climate is key. Such insights are crucial for designing effective climate mitigation strategies and guiding sustainable development in the region.
The impact of soil moisture (SM) and vapor pressure deficit (VPD) on gross primary productivity (GPP) variability in ecosystems is a topic of significant interest. Previous studies have predominantly focused on real-time associations between SM, VPD, and carbon uptake, attributing SM as the principal driver of GPP variability due to its direct and indirect effects through VPD. Using an information theory-based process network approach, we discovered that the influence of past VPD, mediated through its effects on SM, emerges as the primary driver of GPP variability across tropical regions. The past VPD conditions influence GPP directly and also affect SM in real-time alongside GPP, which subsequently impacts GPP variability. Examining land-atmosphere feedback using information theory reveals that past VPD conditions influence SM, but not the reverse. These causal structures explain the consistent decline in GPP with increasing VPD trends observed in tropical regions, which are not consistent with SM trends. Our findings emphasize the importance of considering the influence of past VPD mediated by SM when analyzing complex land-vegetation-atmosphere interactions.
Net Ecosystem Exchange (NEE) measures the net amount of carbon absorbed or released from an ecosystem, which is crucial for understanding the response of vegetation to environmental changes. Previous research has focused more on the daily and monthly variability of NEE and identified the meteorological drivers; however, the biome-specific investigation of sub-daily scale interactions between NEE and micro-meteorological variables remained unexplored. We used 29 FLUXNET sites to examine the short-term fluctuations in weather that significantly influence the carbon exchange, varying widely depending on the time of year and type of ecosystem. Using information theory, we observed that past weather conditions (up to 6 hr of memory) have a more substantial effect on NEE than instantaneous conditions. Specifically, temperature, vapor pressure deficit, and soil water content consistently influenced carbon exchange, while sensible heat and solar radiation had a lesser effect over time. These impacts varied with the seasons but followed a consistent pattern. Our findings suggest that understanding the short-term variations and memory in ecosystems is vital for better predicting how vegetation responds to changing climates.
Soil moisture (SM) plays a critical role in land–atmosphere interaction. Literature shows that the CO2 fertilization effect (CFE) governs vegetation dynamics and its interactions with the atmosphere. However, the impacts of changing vegetation properties due to CFE, on SM, specifically in India, remains unexplored. Comparing Land-hist (observed CO2 concentration) and Land-cCO2 (constant preindustrial CO2 concentration) simulations with the same meteorological forcings from the Land Use Model Inter-comparison Project (LUMIP), we found that changing vegetation due to CFE impacts SM following two pathways. Enhanced water use efficiency (WUE) due to CFE seeks to ameliorate SM by regulating transpiration. Conversely, elevated CO2 increases leaf area index and plant water use, ultimately leading to a decrease in SM due to the combined impacts of CFE in India. This reduction is most prominent in arid regions, where we observe a decline in SM particularly during dry periods. Our study highlights the need to consider plant physiology in land surface modeling under increased CO2 concentration.
Variations in the uptake of atmospheric carbon by vegetation over India, the second-highest contributor to global greening, have enormous implications for climate change mitigation. Global studies conclude that temperature and total water storage (TWS) cause interannual variations of carbon uptake based on the correlation coefficient, which is not a causality measure. Here, we apply a statistically rigorous causality approach, Peter Clark momentary conditional independence, to the monthly observed satellite and station-based gridded dataset of India’s climate and carbon uptake variables. We find no existence of causal connections from TWS to gross primary production (GPP) or net photosynthesis (PSN). Causal relationships exist from precipitation to GPP and PSN. Since shallow-rooted croplands dominate India’s green cover, impacts of precipitation on carbon capture of the the land ecosystem are immediate and not via TWS. Our results identify the key climate drivers of GPP/PSN variability and highlight interactions between water and the carbon cycle in India. Our results also highlight the need for formal causal analysis using climate and earth sciences observations rather than the conventional practices of inferring causality from correlations.
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