Interannual Variability Research Articles

Role of Tropical Disturbances along the South Asian Monsoon Trough in the 2022 Pakistan Floods and the Implications for Interannual Variability

Abstract This study examined the atmospheric circulation patterns that were responsible for the severe flooding that occurred in Pakistan in 2022. It focused on the role of westward-propagating tropical disturbances (TDs) along the South Asian monsoon trough, which are associated with flood-related precipitation events. The study also investigated the interannual precipitation variation in Pakistan over 23 years from 2000 to 2022, using satellite-derived precipitation and atmospheric reanalysis. The results showed high precipitation in southern and central Pakistan, implying tropical influences. The precipitation was the highest in the past 23 years. The massive rainfall was brought by several TDs under the highest water vapor conditions, although a TD of South Asia climatologically tends to weaken before affecting Pakistan. Enhanced easterlies along the Indo-Gangetic Plain, part of the enhanced monsoon trough, supported high water vapor conditions over Pakistan. The high water vapor was also partly supported by the long-lasting active phase of the South Asian monsoon intraseasonal oscillation in 2022. Moreover, the La Niña condition increased moisture transport as a background. On an interannual time scale, the enhanced moisture flux over the Indo-Gangetic Plain resulted in higher precipitation over Pakistan, as occurred in 2022. These high (low) water vapor and precipitation can be partly explained by La Niña (El Niño). However, La Niña and El Niño may not control the TD activity over South Asia. Thus, for 2022, the high water vapor is somewhat empirically predictable, but the active TD activity can be coincidental. Significance Statement In 2022, Pakistan was hit by catastrophic flooding that affected 15% of the population. The analysis of the atmospheric circulation associated with the flooding indicates that weak but rain-bearing tropical disturbances played a significant role in flooding. Several tropical disturbances stayed or moved into Pakistan, bringing massive rainfall although, usually, they tend to weaken before hitting Pakistan. In the past 23 years, La Niña–related weather patterns have partly explained the year-to-year precipitation variation in Pakistan. However, in 2022, the unusually heavy flooding was due to the combined La Niña effect and tropical disturbance activity. Because it is still difficult to predict the seasonal tropical disturbance activity from La Niña, El Niño, and other climatic conditions, seasonal prediction of these floods remains challenging.

Dynamics of Sandy Shorelines and Their Response to Wave Climate Change in the East of Hainan Island, China

Beach erosion and shoreline dynamics are strongly affected by alterations in nearshore wave intensity and energy, especially in the context of global climate change. However, existing works do not thoroughly study the evolution of the sandy coasts of eastern Hainan Island, China, nor their responses to wave climate change driven by climate variability. This study focuses on the open sandy coast and assesses shoreline evolutionary dynamics in response to wave climate variability over a 30-year period from 1994 to 2023, using an open-source software toolkit that semi-automatically identify the shorelines (CoastSat v2.4) and reanalysis wave datasets (ERA5). The shorelines of the study area were extracted from CoastSat, and then tidal correction and outlier correction were performed for clearer shorelines. Combining the shoreline changes and wave conditions derived from ERA5, the dynamics of the shorelines and their response to wave climate change were further studied. The findings reveal that the average long-term shoreline change rate along the eastern coast of Hainan Island is 0.03 m/year, with 44.8% of transects experiencing erosion and 55.2% showing long-term accretion. And distinct evolutionary patterns emerge across different sections. Interannual variability is marked by alternating erosion and siltation cycles, while most sections of the coast experiences clear seasonal fluctuations, with accretion typically occurring during summer and erosion occurring in winter. El Niño–Southern Oscillation (ENSO) cycles drive changes in parameters including significant wave height, mean wave period, wave energy flux, and mean wave direction, leading to long-term changes in wave climate. The multi-scale behavior of the sandy shoreline responds distinctly to the ongoing changes in wave climate triggered by ENSO viability, with El Niño events typically resulting in accretion and La Niña periods causing erosion. Notably, mean wave direction is the metric most closely linked to changes in the shoreline among all the others. In conclusion, the interplay of escalating anthropogenic activities, natural processes, and climate change contributes to the long-term evolution of sandy shorelines. We believe this study can offer a scientific reference for erosion prevention and management strategies of sandy beaches, based on the analysis presented above.

EVALUATION OF DEFORESTATION IN THE MUNICIPALITY OF BRASIL NOVO IN THE STATE OF PARÁ - BRAZIL, USING MACHINE LEARNING TECHNIQUES

The agricultural frontier of the Brazilian Amazon, is undergoing significant changes in its land cover, primarily driven by the expansion of cattle ranching and cocoa cultivation. Through the analysis of Landsat 8 satellite images and the WorldCover 10m 2020 product, combined with climatic and socioeconomic data, this study investigated land use dynamics between 2015 and 2023, on the municipality of Brasil Novo. The results revealed a substantial reduction in forest cover, with the expansion of pasturelands as the main driver of deforestation. The temporal analysis of the Normalized Difference Vegetation Index (NDVI) indicated interannual variations in vegetation cover, associated with extreme weather events and anthropogenic pressure. Although the municipality still maintains 43.8% of forest cover, with the Arara Indigenous Land being a notable example, the pressure on natural resources demands the implementation of public policies and sustainable management practices to mitigate environmental impacts and ensure the conservation of Amazonian biodiversity. Keywords: Amazon, NDVI, remote sensing, climate change, livestock, land use.

Drivers of Spatiotemporal Patterns of Riparian Forest NDVI Along a Hydroclimatic Gradient

ABSTRACTIn the context of rising global temperatures and their impact on weather patterns and water cycles, understanding the relationship between vegetation and hydroclimatic forcing is critical. Riparian forests are highly vulnerable to hydroclimatic variability, which can significantly affect water availability in the soil on which they primarily depend. Along large rivers, hydroclimatic forcings can vary, resulting in different vegetative responses depending on the local climatic context and site conditions. To explore this, we studied riparian forest greenness along a 512‐km river corridor with a 3° latitudinal gradient, analysing the relative contributions of climate (latitude, season, temperature, precipitation) and local hydrological conditions (groundwater). Here, we show that riparian forests along a latitudinal gradient respond differently to hydroclimatic controls, with vegetative dynamics that can be attenuated or accentuated by local site conditions. We combined Sentinel‐2 satellite Normalised Difference Vegetation Index (NDVI) data over seven years (2016–2022) with hydroclimatic data to examine riparian forest greenness responses to latitudinal, seasonal and interannual hydroclimatic variability. We found contrasting hydroclimatic controls across the latitudinal gradient, with the northernmost sites predominantly controlled by temperature, while those further south are limited by water availability. In addition, we identified temperature as the primary driver of NDVI throughout the growing season, either positively or negatively. Late season precipitation and high phreatic water availability positively influenced NDVI, emphasising the role of local conditions in governing riparian forest resilience. This study enhances understanding of climate controls on riparian tree greenness, which is critical for designing effective landscape‐scale riparian ecosystem management and restoration strategies.

Reconstructing tropical monthly sea surface temperature variability by assimilating coral proxy datasets

Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.

North Atlantic Subtropical Mode Water properties: intrinsic and atmospherically forced interannual variability

Abstract. This study investigates the contributions of the ocean's chaotic intrinsic variability (CIV) and atmospherically forced variability to the interannual fluctuations in the North Atlantic Subtropical Mode Water (STMW) properties. Utilizing a 0.25° regional 50-member ocean–sea-ice ensemble simulation driven by an original surface forcing method and perturbed initially, the forced variability in STMW properties is estimated from ensemble mean fluctuations, while CIV is determined from deviations around the ensemble mean within each member. The model successfully captures the main features of STMW, showing correct agreement with observation-based ARMOR3D data in terms of location, seasonality, mean temperature and volume, as well as interannual variance of STMW's main properties. CIV significantly impacts STMW, explaining 10 %–13 % and 28 %–44 % of the interannual variance of its geometric and thermohaline mean properties, respectively, with a maximum imprint on STMW temperature. Observation-based and simulated intrinsic-to-total variance ratios are mostly consistent, dispelling concerns about a signal-to-noise paradox. This study also illustrates the advantages of ensemble simulations over single simulations in understanding oceanic fluctuations and attributing them to external drivers, while also cautioning against overreliance on individual simulation assessments.

Spatiotemporal variability of flash floods and their human impacts in the Czech Republic during the 2001–2023 period

Abstract. Flash floods, characterized by their sudden onset, extreme discharge, short duration, material damage, and human loss, represent a significant natural hazard. Not well covered by standard hydrological observations, flash flood data can primarily be derived from various types of documentary evidence. This evidence served as the main data source for creating a flash flood database for the Czech Republic from 2001 to 2023. This database enabled detailed analysis of different aspects of flash floods. The annual series of 233 flash flood events, 160 flash flood days, and 424 affected municipalities showed significant inter-annual variability but no linear trends. The triggering rainfall that generates flash floods was analyzed with respect to 1–3-hourly and daily precipitation totals and circulation types from the objective classification. While flash floods can occur anywhere, they were more frequently recorded at the foots of mountain slopes, often coinciding with “critical points” where built-up areas meet concentrated surface runoff pathways. The division of material damage caused by flash floods into eight categories indicated that the highest proportion of damage was to streets and communications (24.3 %), to houses (21.7 %), and to their cellars and basements (18.3 %). There were also 36 recorded fatalities. The understanding of flash floods in the Czech Republic aligns generally well with studies of flash floods in other European regions.

Spatial-temporal variations and Attribution analysis of SO2 concentration in Beijing-Tianjin-Hebei regions from 2013 to 2022

Based on the monitoring data of air pollutants in Beijing-Tianjin-Hebei regions from 2012 to 2022, the spatial-temporal distribution of SO2 concentration and main influencing factors were analyzed through multi-methods of Daniel trend test, KZ filtering and WRF/CMAQ model simulations. From 2013 to 2022, the annual averaged concentrations of SO2 in Beijing, Tianjin and Hebei regions were all in obvious downward trends, and all passed the Daniel trend test (α=0.01). The annual averaged concentration of SO2 in Beijing, Tianjin and Hebei regions decreased from 26.6~114.3μg·m-3 in 2013 to 3.0~11.0μg·m-3, with the decline rates fluctuating from 2.62~11.81μg/(m3·yr) while the decrease rate of SO2 in Beijing was the lowest. The average annual concentration of SO2 in Tangshan city decreased the most, reaching 92.3% and it changed to be the smallest, about 78.2% in Chengde City. The reduction of coal consumption in the Beijing-Tianjin-Hebei regions significantly reduced atmospheric SO2 emissions and directly reduced the regional average concentration of SO2. Meteorological factors were generally conducive to the diffusion of SO2 concentration during 2013~2022, and the meteorological factors contributed about 0.4~5.5% in 13 cities in Jingjinji area while the contribution of anthropogenic emission reduction was 94.5~99.6%. The inter-annual variation of meteorological factors in summer from 2013 to 2022 was not conducive to the diffusion of SO2 concentration on the whole, and the contribution changed to -52.6 ~ -1.0% in 13 cities. From June to August in summer under the easterly and southerly winds, the SO2 concentration in Beijing was basically twice that of the northerly and westerly winds. The contribution of southwest transport channel to SO2 concentration in Beijing was 33.5%, while it changed to 38.9% in the southeast transport channel. Therefore, strengthening the management and control of air pollutants in surrounding area of Beijing is helpful for Beijing to further reduce the levels of air pollutants and achieve targets of air quality improvement in the 14th Five-Year Plan period.

Scale‐Dependent Drivers of Air‐Sea CO2 Flux Variability

AbstractIn climate studies, it is crucial to distinguish between changes caused by natural variability and those resulting from external forcing. Here we use a suite of numerical experiments based on the ECCO‐Darwin ocean biogeochemistry model to separate the impact of the atmospheric carbon dioxide (CO2) growth rate and climate on the ocean carbon sink — with a goal of disentangling the space‐time variability of the dominant drivers. When globally integrated, the variable atmospheric growth rate and climate exhibit similar magnitude impacts on ocean carbon uptake. At local scales, interannual variability in air‐sea CO2 flux is dominated by climate. The implications of our study for real‐world ocean observing systems are clear: in order to detect future changes in the ocean sink due to slowing atmospheric CO2 growth rates, better observing systems and constraints on climate‐driven ocean variability are required.

Climatic and edaphic controls of root-tip production and mortality in five temperate tree species

Root tips are the main components of absorptive fine roots, but their seasonal dynamics and relationship to environmental factors remain unclear due to the difficulties in methodology. In this study, we explored the temporal patterns of root-tip production and mortality in monoculture plantations of five temperate tree species at a common site in northeastern China, and identified the general environmental controls on such processes. We made monthly in-situ assessments of root tip length (RTL) production and mortality in two hardwood and three coniferous species with a minirhizotron (MR) method during the growing seasons of 2008 and 2009. Air temperature, rainfall, soil temperature and water content at 10 cm depth were determined concurrently. RTL production in all species exhibited consistent peaks in summer (June–August) in two growing seasons. RTL mortality showed substantial interannual and interspecific variability, with peaks in autumn and winter in 2008, but various patterns in 2009. RTL production positively correlated with monthly soil and air temperature across all species, and with monthly rainfall in three coniferous species. However, there was no significant correlation between RTL production and soil water content. By contrast, RTL mortality was weakly related to environmental factors, showing positive correlations with soil temperature in Korean spruce, and with rainfall in Korean pine and Korean spruce. Our findings suggest that the seasonal patterns of RTL production are convergent across the five temperate tree species due to the overlapped distribution of heat and rainfall, which can conduce roots to maximizing the acquisition of nutrient resources in the soil.

Impact of ocean vertical-mixing parameterization on Arctic sea ice and upper-ocean properties using the NEMO-SI3 model

Abstract. We evaluate the vertical turbulent-kinetic-energy (TKE) mixing scheme of the NEMO-SI3 ocean–sea-ice model in sea-ice-covered regions of the Arctic Ocean. Specifically, we assess the parameters involved in TKE mixed-layer-penetration (MLP) parameterization. This ad hoc parameterization aims to capture processes that impact the ocean surface boundary layer, such as near-inertial oscillations, ocean swells, and waves, which are often not well represented in the default TKE scheme. We evaluate this parameterization for the first time in three regions of the Arctic Ocean: the Makarov, Eurasian, and Canada basins. We demonstrate the strong effect of the scaling parameter that accounts for the presence of sea ice. Our results confirm that TKE MLP must be scaled down below sea ice to avoid unrealistically deep mixed layers. The other parameters evaluated are the percentage of energy penetrating below the mixed layer and the length scale of its decay with depth. All these parameters affect mixed-layer depth and its seasonal cycle, surface temperature, and salinity, as well as underlying stratification. Shallow mixed layers are associated with stronger stratification and fresh surface anomalies, and deeper mixed layers correspond to weaker stratification and salty surface anomalies. Notably, we observe significant impacts on sea-ice thickness across the Arctic Ocean in two scenarios: when the scaling parameter due to sea ice is absent and when the TKE mixed-layer-penetration process vanishes. In the former case, we observe an increase of several meters in mixed-layer depth, along with a reduction in sea-ice thickness ranging from 30 to 40 cm, reflecting the impact of stronger mixing. Conversely, in the latter case, we notice that a shallower mixed layer is accompanied by a moderate increase in sea-ice thickness, ranging from 10 to 20 cm, as expected from weaker mixing. Additionally, interannual variability suggests that experiments incorporating a scaling parameter based on sea-ice concentration display an increased mixed-layer depth during periods of reduced sea ice, which is consistent with observed trends. These findings underscore the influence of enhanced ocean mixing, through specific parameterizations, on the physical properties of the upper ocean and sea ice.

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

Evaluating water quality of rock glacier outflows in the Western Alps, Italy: a regional perspective.

Intact rock glaciers (RG) are considered valuable water storage because containing permafrost ice volumes. The hydrological relevance of RG is forecasted to increase with respect to glaciers under climate change scenarios, as well as RG's role as water resources in alpine basins for multiple uses. Besides the assessment of water amount stored in intact rock glaciers, the evaluation of water quality is of primary importance. Here, we present the results of a chemical survey performed on five outflows from intact RG in 2020-2023 in the Piedmont region, Western Alps, Italy, along a latitudinal gradient and in different geological settings. The survey aimed to assess the water quality of RG outflows based on chemical indicators (major ions, nutrients, trace metals). Sampling and analyses were performed according to standard methods for freshwater samples, paying specific attention to the analytical quality and consistency of the data. We considered seasonal and interannual variability of the main chemical variables and the possible effects of RG outflows on the chemistry of lakes and ponds located in proximity to the RG. All the investigated sites were characterized by low to moderate ion content, low nutrients, and trace metals close to or below the detection limit, indicating a good water quality status. Results suggested lithology as the main factor affecting the chemical composition of RG outflows. The results of this study indicate it is advisable to develop shared protocols and joint monitoring programs for data collecting at RG outflow sites all over the Alps, possibly integrating chemical and biological indicators, with the final aim of monitoring the water quality of these valuable resources and its temporal evolution under climate change.

Cross-continental comparison of plant reproductive phenology shows high intraspecific variation in temperature sensitivity

Abstract The success of plant species under climate change will be determined, in part, by their phenological responses to temperature. Despite the growing need to forecast such outcomes across entire species ranges, it remains unclear how phenological sensitivity to temperature might vary across individuals of the same species. In this study, we harnessed community science data to document intraspecific patterns in phenological temperature sensitivity across the multicontinental range of six herbaceous plant species. Using linear models, we correlated georeferenced temperature data with 23,220 plant phenological records from iNaturalist to generate spatially-explicit estimates of phenological temperature sensitivity across the shared range of species. We additionally evaluated the geographic association between local historic climate conditions (i.e. mean annual temperature and interannual variability in temperature) and the temperature sensitivity of plants. We found that plant temperature sensitivity varied substantially at both the interspecific and intraspecific level, demonstrating that phenological responses to climate change have the potential to vary both within and among species. Additionally, we provide evidence for a strong geographic association between plant temperature sensitivity and local historic climate conditions. Plants were more sensitive to temperature in hotter climates (i.e., regions with high mean annual temperature), but only in regions with high interannual temperature variability. In regions with low interannual temperature variability, plants displayed universally weak sensitivity to temperature, regardless of baseline annual temperature. This evidence suggests that pheno-climatic forecasts may be improved by accounting for intraspecific variation in phenological temperature sensitivity. Broad climatic factors such as mean annual temperature and interannual temperature variability likely serve as useful predictors for estimating temperature sensitivity across species’ ranges.

Evaluation of CMIP5 and CMIP6 Models Based on Weather Types Applied to the South Atlantic Ocean

ABSTRACTChanges in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios.

Advancing Subseasonal Surface Air Temperature and Heat Wave Prediction Skill in China by Incorporating Scale Interaction in a Deep Learning Model

AbstractAccurate subseasonal predictions of high surface air temperature (SAT) and heat wave events 10–30 days in advance are crucial for mitigating the risks of extreme weather; however, they pose a challenge for current operational models. In this study, we trained a convolutional neural network (CNN)‐based deep learning model to exploit the modulations in China's SAT by precursor signals across different timescales to improve predictions of future SAT and heat wave events. This CNN model demonstrated superior capability in capturing the evolution of SAT anomalies and the occurrence of heat wave events with forecast lead times beyond 20 days, compared with that of the operational models of the China Meteorological Administration and European Centre for Medium‐Range Weather Forecasts. Explainability analysis highlighted that subseasonal SAT predictability in China is driven primarily by large‐scale intraseasonal perturbations from both lower‐ and higher‐latitude regions of Eurasia and interannual variability.

Hydrological classification of mine pit lakes using modelling experiments

With the growing global prevalence of open-pit mining activities, there is an increasing necessity for sustainable mine life cycle plans with an early outlook towards mine closure. A major consideration in mine closure planning is the potential formation of lakes in the mine void and how these “pit lakes” can be managed to minimise risks and, if possible, create benefits. Understanding the long-term interactions between pit lakes, groundwater, and surface water systems is essential for that purpose. While numerous site-specific studies have been undertaken, there have been no studies that aim to provide a general, broadly applicable understanding of how pit lake hydrology relates to geographical context, and no efforts to hydrologically classify pit lakes using geographical criteria. This research employs an integrated generic pit lake water balance model to examine mine pit lake interactions with the surrounding surface water and groundwater. Simulating 243 scenarios, the influence of five input factors (climate, hydraulic conductivity, regional groundwater level, catchment area and pit slope) on pit lake behaviour up to 6000 years beyond the closure of the mine was considered. The assessment focused on four pit lake hydrological attributes once the system reached equilibrium: water level, time to equilibrium, the fraction of days where the pit recharges groundwater, and the fraction of days where there is surface overflow from the lake. All scenarios were assigned to one of five hydrological classes based on the interactions between the pit lake and the surrounding surface water and groundwater. Our findings show that, in many contexts, general data on climate type and subsurface hydraulic conductivity can yield reliable predictions of a pit lake's long-term hydrological classification without having to develop a detailed, site-specific pit lake model. The classification needs improvements in non-arid climates where inter-annual variation in rainfall is pronounced. The pit lake classification is particularly valuable for first-pass risk assessments to determine whether site-specific modelling is required.

Patterns of foliar fungal diseases and the effects on aboveground biomass in alpine meadow under simulated climate change

1.Global warming and changes in precipitation patterns significantly affect plant fungal diseases, especially plant foliar fungal diseases. Research surrounding the effects of climate change on plants are mostly concentrated on aboveground vegetation and belowground soil microorganisms, with limited attention given to foliar fungal disease. Additionally, little is known whether climate change experiments can simulate the impact of variations on foliar diseases within real natural communities.2.We conducted a long-term experiment to investigate the effect of warming and altered precipitation on plant disease in an alpine grassland on the north-eastern Tibetan Plateau. Our study tracked foliar disease across 26 species of plants including non-legume forbs, legumes, grasses and sedges from 2019 to 2021. Each species was included in a two-factor full factorial experiment manipulating both temperature and precipitation.3.In our study the non-legume forb functional group carried the primary pathogen load, sedges maintained a near zero pathogen load across all investigated scenarios. Warming significantly elevated the pathogen load in legumes and non-legume forbs, while it reduced the pathogen load of grasses. Conversely, decreased precipitation significantly increased the pathogen load of non-legume forbs and increased the pathogen load of grasses. Notably, we found that pathogen load was primarily influenced by the direct effects of warming and precipitation, rather than indirectly through changes in plant community structure. Meanwhile, interannual climate variability exhibited a strong correlation with plant community pathogen loads. Areas experiencing increased temperature and precipitation showed lower fungal disease pressure, whereas regions with elevated temperatures combined with decreased precipitation faced a heightened risk of disease in alpine meadows on the Qinghai-Tibet Plateau.4.Our findings reveal that warming and decreased precipitation led to a reduction in above-ground biomass associated with increased the pathogen load of legumes. This underscores the importance of pathogen effects in both climate change prediction and ecological management.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Impact of Solar Radiation Management on Andean glacier-wide surface mass balance

Solar Radiation Management (SRM) is a climate intervention strategy aimed at mitigating global warming by reducing incoming solar radiation. We investigate the potential influence of SRM on Andean glacier-wide surface mass balance, as glaciers are crucial for downstream ecological functions along the west coast of South America. We numerically simulate the surface mass balance response of thousands of glaciers to SRM and other climate change scenarios throughout the 21st century. Results indicate Pan-Andean negative mass balance, irrespective of the scenario or glacier-climate regimes. However, SRM tends to modify interannual variability and temperature sensitivity in several regions. Our findings also suggest that if SRM had been implemented in the late 1980’s, it would have been effective in avoiding the negative trajectory seen today. The Andes feature nearly all mountain hydroclimatic regimes, representing a globally relevant example of SRM impacts. These findings emphasize the urgency of drastic emissions reduction.