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Abstract Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. Here, we employed a microbial ecological model (MEND), driven by downscaled output data from six Earth system models under two Shared Socio‐economic Pathways (SSP1‐2.6 and SSP5‐8.5) scenarios, to simulate long‐term soil biogeochemical processes. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). For the entire soil layer in both forests, we found that, compared to the baseline period of 2009–2020, the mean SOC during 2081–2100 increased by 40.9%–90.6% under the L or T change scenarios, versus 5.2%–31.0% under the W change scenario. However, soil moisture emerged as a key regulator of SOC, MBC and inorganic N dynamics in the topsoil of BF and PF. For example, W change led to SOC gain of 5.5%–37.2%, compared to the SOC loss of 15.5%–18.0% under L or T scenario. Additionally, a further reduction in soil pH by 0.2 units in the BF, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the LTW (simultaneous changes in the three factors) scenario. These results indicate that the results derived solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. , Plain Language Summary Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). We found that soil moisture change would be beneficial for SOC accumulation and serves as a key regulator of MBC and inorganic N in topsoil, whereas the change in litterfall or soil temperature are favorable for SOC accumulation in the entire soil profile. Additionally, a further reduction in soil pH by 0.2 units, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the scenario with simultaneous changes in L, W, and T. These results indicate that findings solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. , Key Points Soil C responses to climate change are depth dependent, therefore, results from just the topsoil may not apply to the entire soil profile Soil moisture change benefits topsoil SOC accumulation, whereas litterfall and soil temperature changes favor SOC accumulation in the entire soil profile We need to pay more attention to the effects of soil moisture and pH rather than temperature and litter‐input on soil biogeochemical processes

Abstract Forest above‐ground biomass (AGB) is often estimated by converting the observed tree size using allometric scaling between the dry weight and size of an organism. However, the variations in biomass allocation and scaling between tree crowns and stems due to survival competition during a tree's lifecycle remain unclear. This knowledge gap can improve the understanding of modelling tree biomass allometry because traditional allometries ignore the dynamics of allocation. Herein, we characterised allometric scaling using the dynamic ratio ( r ) of the stem biomass (SB) to AGB and a dynamic exponent. The allometric models were biologically parameterised by the r values for initial, intermediate and final ages rather than only a regression result. The scaling was tested using field measurements of 421 species and 2213 different‐sized trees in pantropical regions worldwide. We found that the scaling fluctuated with tree size, and this fluctuation was driven by the trade‐off relationship of biomass allocation between the tree crown and stem depending on the dynamic crown trait. The allometric scaling between SB and AGB varied from 0.8 to 1.0 for a tree during its entire lifecycle. The fluctuations presented a general law for the allometric scaling of the pantropical tree biomass and size. Our model quantified the trade‐off and explained 94.1% of the allometric relationship between the SB and AGB (93.8% of which between D 2 H and AGB) for pantropical forests, which resulted in a better fit than that of the traditional model. Considering the effects of the trade‐off on modelling, the actual biomass of large trees could be substantially greater than conventional estimates. These results highlight the importance of coupling growth mechanisms in modelling allometry and provide a theoretical foundation for better describing and predicting forest carbon accumulation.

Abstract Litter decomposition is a key ecological process that determines carbon (C) and nutrient cycling in terrestrial ecosystems. The initial concentrations of C and nutrients in litter play a critical role in this process, yet the global patterns of litter initial concentrations of C, nitrogen (N) and phosphorus (P) are poorly understood. We employed machine learning with a global database to quantitatively assess the global patterns and drivers of leaf litter initial C, N and P concentrations, as well as their returning amounts (i.e. amounts returned to soils). The medians of litter C, N and P concentrations were 46.7, 1.1, and 0.1%, respectively, and the medians of litter C, N and P returning amounts were 1.436, 0.038 and 0.004 Mg ha −1  year −1 , respectively. Soil and climate emerged as the key predictors of leaf litter C, N and P concentrations. Predicted global maps showed that leaf litter N and P concentrations decreased with latitude, while C concentration exhibited an opposite pattern. Additionally, the returning amounts of leaf litter C, N and P all declined from the equator to the poles in both hemispheres. Synthesis : Our results provide a quantitative assessment of the global concentrations and returning amounts of leaf litter C, N and P, which showed new light on the role of leaf litter in global C and nutrients cycling.

Abstract. The amount and the phase of cold-season precipitation accumulating in the upper Saint John River (SJR) basin are critical factors in determining spring runoff, ice jams, and flooding. To study the impact of winter and spring storms on the snowpack in the upper SJR basin, the Saint John River Experiment on Cold Season Storms (SAJESS) was conducted during winter–spring 2020–2021. Here, we provide an overview of the SAJESS study area, field campaign, and data collected. The upper SJR basin represents 41 % of the entire SJR watershed and encompasses parts of the US state of Maine and the Canadian provinces of Quebec and New Brunswick. In early December 2020, meteorological instruments were co-located with an Environment and Climate Change Canada station near Edmundston, New Brunswick. This included a separate weather station for measuring standard meteorological variables, an optical disdrometer, and a micro rain radar. This instrumentation was augmented during an intensive observation period that also included upper-air soundings, surface weather observations, a multi-angle snowflake camera, and macrophotography of solid hydrometeors throughout March and April 2021. During the study, the region experienced a lower-than-average snowpack that peaked at ∼ 65 cm, with a total of 287 mm of precipitation (liquid-equivalent) falling between December 2020 and April 2021, a 21 % lower amount of precipitation than the climatological normal. Observers were present for 13 storms during which they conducted 183 h of precipitation observations and took more than 4000 images of hydrometeors. The inclusion of local volunteers and schools provided an additional 1700 measurements of precipitation amounts across the area. The resulting datasets are publicly available from the Federated Research Data Repository at https://doi.org/10.20383/103.0591 (Thompson et al., 2023). We also include a synopsis of the data management plan and a brief assessment of the rewards and challenges of conducting the field campaign and utilizing community volunteers for citizen science.

Abstract. In this review, we assess scientific evidence for tipping points in ocean and atmosphere circulations. The warming of oceans, modified wind patterns and increasing freshwater influx from melting ice hold the potential to disrupt established circulation patterns. The literature provides evidence for oceanic tipping points in the Atlantic Meridional Overturning Circulation (AMOC), the North Atlantic Subpolar Gyre (SPG), and the Antarctic Overturning Circulation, which may collapse under warmer and ‘fresher’ (i.e. less salty) conditions. A slowdown or collapse of these oceanic circulations would have far-reaching consequences for the rest of the climate system and could lead to strong impacts on human societies and the biosphere. Among the atmospheric circulation systems considered, we classify the West African monsoon as a tipping system. Its abrupt changes in the past have led to vastly different vegetation states of the Sahara (e.g. “green Sahara” states). Evidence about tipping of the monsoon systems over South America and Asia is limited however, there are multiple potential sources of destabilisation, including large-scale deforestation, air pollution, and shifts in other circulation patterns (in particular the AMOC). Although theoretically possible, there is currently little indication for tipping points in tropical clouds or mid-latitude atmospheric circulations. Similarly, tipping towards a more extreme or persistent state of the El Niño-Southern Oscillation (ENSO) is currently not fully supported by models and observations. While the tipping thresholds for many of these systems are uncertain, tipping could have severe socio-environmental consequences. Stabilising Earth’s climate (along with minimising other environmental pressures, like aerosol pollution and ecosystem degradation) is critical for reducing the likelihood of reaching tipping points in the ocean-atmosphere system.

Satellite data are vital for understanding the large-scale spatial distribution of particulate matter (PM 2.5 ) due to their low cost, wide coverage, and all-weather capability. Estimation of PM 2.5 using satellite aerosol optical depth (AOD) products is a popular method. In this paper, we review the PM 2.5 estimation process based on satellite AOD data in terms of data sources (i.e., inversion algorithms, data sets, and interpolation methods), estimation models (i.e., statistical regression, chemical transport models, machine learning, and combinatorial analysis), and modeling validation (i.e., four types of cross-validation (CV) methods). We found that the accuracy of time-based CV is lower than others. We found significant differences in modeling accuracy between different seasons ( p < 0.01) and different spatial resolutions ( p < 0.01). We explain these phenomena in this article. Finally, we summarize the research process, present challenges, and future directions in this field. We opine that low-cost mobile devices combined with transfer learning or hybrid modeling offer research opportunities in areas with limited PM 2.5 monitoring stations and historical PM 2.5 estimation. These methods can be a good choice for air pollution estimation in developing countries. The purpose of this study is to provide a basic framework for future researchers to conduct relevant research, enabling them to understand current research progress and future research directions.

Abstract Northeast Brazil and Western Africa are two regions geographically separated by the Atlantic Ocean, both home to vulnerable populations living in semi-arid areas. Atlantic Ocean modes of variability and their interactions with the atmosphere are the main drivers of decadal precipitation in these Atlantic Ocean coastal areas. How these low-frequency modes of variability evolve and interact with each other is key to understanding and predicting decadal precipitation. Here we use the Self-Organizing Maps neural network with different variables to unravel causality between the Atlantic modes of variability and their interactions with the atmosphere. Our study finds an 82% (p<0.05) anti-correlation between decadal rainfall in Northeast Brazil and Western Africa from 1979 to 2005. We also find three multi-decadal cycles: 1870-1920, 1920-1970, and 1970-2019 (satellite era), pointing to a 50-year periodicity governing the sea surface temperature anomalies of Tropical and South Atlantic. Our results demonstrate how Northeast Brazil and Western Africa rainfall anti-correlation was formed in the satellite era and how it might be part of a 50-year cycle from the Tropical and South Atlantic decadal variability.

Background: Few studies have explored how vector control interventions may modify associations between environmental factors and malaria. Methods: We used weekly malaria cases reported from six public health facilities in Uganda. Environmental variables (temperature, rainfall, humidity, and vegetation) were extracted from remote sensing sources. The non-linearity of environmental variables was investigated, and negative binomial regression models were used to explore the influence of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) on associations between environmental factors and malaria incident cases for each site as well as pooled across the facilities, with or without considering the interaction between environmental variables and vector control interventions. Results: An average of 73.3 weekly malaria cases per site (range: 0–597) occurred between 2010 and 2018. From the pooled model, malaria risk related to environmental variables was reduced by about 35% with LLINs and 63% with IRS. Significant interactions were observed between some environmental variables and vector control interventions. There was site-specific variability in the shape of the environment–malaria risk relationship and in the influence of interventions (6 to 72% reduction in cases with LLINs and 43 to 74% with IRS). Conclusion: The influence of vector control interventions on the malaria–environment relationship need to be considered at a local scale in order to efficiently guide control programs.