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Abstract. The changing Arctic climate is creating increased economic, transportation, and recreational activities requiring reliable and relevant weather information. However, the Canadian Arctic is sparsely observed, and processes governing weather systems in the Arctic are not well understood. There is a recognized lack of meteorological data to characterize the Arctic atmosphere for operational forecasting and to support process studies, satellite calibration/validation, search and rescue operations (which are increasing in the region), high-impact weather (HIW) detection and prediction, and numerical weather prediction (NWP) model verification and evaluation. To address this need, Environment and Climate Change Canada commissioned two supersites, one in Iqaluit (63.74∘ N, 68.51∘ W) in September 2015 and the other in Whitehorse (60.71∘ N, 135.07∘ W) in November 2017 as part of the Canadian Arctic Weather Science (CAWS) project. The primary goals of CAWS are to provide enhanced meteorological observations in the Canadian Arctic for HIW nowcasting (short-range forecast) and NWP model verification, evaluation, and process studies and to provide recommendations on the optimal cost-effective observing system for the Canadian Arctic. Both sites are in provincial/territorial capitals and are economic hubs for the region; they also act as transportation gateways to the north and are in the path of several common Arctic storm tracks. The supersites are located at or next to major airports and existing Meteorological Service of Canada ground-based weather stations that provide standard meteorological surface observations and upper-air radiosonde observations; they are also uniquely situated in close proximity to frequent overpasses by polar-orbiting satellites. The suite of in situ and remote sensing instruments at each site is completely automated (no on-site operator) and operates continuously in all weather conditions, providing near-real-time data to operational weather forecasters, the public, and researchers via obrs.ca. The two sites have similar instruments, including mobile Doppler weather radars, multiple vertically profiling and/or scanning lidars (Doppler, ceilometer, water vapour), optical disdrometers, precipitation gauges in different shielded configurations, present weather sensors, fog monitoring devices, radiation flux sensors, and other meteorological instruments. Details on the two supersites, the suites of instruments deployed, the data collection methods, and example case studies of HIW events are discussed. CAWS data are publicly accessible via the Canadian Government Open Data Portal (https://doi.org/10.18164/ff771396-b22c-4bc3-844d-38fc697049e9, Mariani et al., 2022a, and https://doi.org/10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78, Mariani et al., 2022b); this dataset is being used to improve our understanding of synoptic and fine-scale meteorological processes in the Arctic and sub-Arctic, including HIW detection and prediction and NWP verification, assimilation, and processes.

Abstract. Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment – European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models.

The Australian Alps are the highest mountain range in Australia, which are important for biodiversity, energy generation and winter tourism. Significant increases in temperature in the past decades has had a huge impact on biodiversity and ecosystem in this region. In this study, observed temperature is used to assess how temperature changed over the Australian Alps and surrounding areas. We also use outputs from two generations of NARCliM (NSW and Australian Regional Climate Modelling) to investigate spatial and temporal variation of future changes in temperature and its extremes. The results show temperature increases faster for the Australian Alps than the surrounding areas, with clear spatial and temporal variation. The changes in temperature and its extremes are found to be strongly correlated with changes in albedo, which suggests faster warming in cool season might be dominated by decrease in albedo resulting from future changes in natural snowfall and snowpack. The warming induced reduction in future snow cover in the Australian Alps will have a significant impact on this region.

Abstract Inconsistencies between Holocene climate reconstructions and numerical model simulations question the robustness of climate models and proxy temperature records. Climate reconstructions suggest an early-middle Holocene Thermal Maximum (HTM) followed by gradual cooling, whereas climate models indicate continuous warming. This discrepancy either implies seasonal biases in proxy-based climate reconstructions, or that the climate model sensitivity to forcings and feedbacks needs to be reevaluated. Here, we analyze a global database of Holocene paleotemperature records to investigate the spatiotemporal structure of the HTM. Continental proxy records at mid and high latitudes of the Northern Hemisphere portray a “classic” HTM (8–4 ka). In contrast, marine proxy records from the same latitudes reveal an earlier HTM (11–7ka), while a clear temperature anomaly is missing in the tropics. The results indicate a heterogeneous response to climate forcing and highlight the lack of globally synchronous HTM.

The biogeophysical effects of severe forestation are quantified using a new ensemble of regional climate simulations over North America and Europe. Following the protocol outlined for the Land-Use and Climate Across Scales (LUCAS) intercomparison project, two sets of simulations are compared, FOREST and GRASS, which respectively represent worlds where all vegetation is replaced by trees and grasses. Three regional climate models were run over North America. One of them, the Canadian Regional Climate Model (CRCM5), was also run over Europe in an attempt to bridge results with the original LUCAS ensemble, which was confined to Europe. Overall, the CRCM5 response to forestation reveals strong inter-continental similarities, including a pronounced wintertime and springtime warming concentrated over snow-masking evergreen forests. Crucially, these northern evergreen needleleaf forests populate lower, hence sunnier, latitudes in North America than in Europe. Snow masking reduces albedo similarly over both continents, but stronger insolation amplifies the net shortwave radiation and hence warming simulated over North America. In the summertime, CRCM5 produces a mixed response to forestation, with warming over northern needleleaf forests and cooling over southern broadleaf forests. The partitioning of the turbulent heat fluxes plays a major role in determining this response, but it is not robust across models over North America. Implications for the inter-continental transferability of the original LUCAS results are discussed.

Background: Although floods may have important respiratory health impacts, few studies have examined this issue. This study aims to document the long-term impacts of the spring floods of 2019 in Quebec by (1) describing the population affected by the floods; (2) assessing the impacts on the respiratory system according to levels of exposure; and (3) determining the association between stressors and respiratory health. Methods: A population health survey was carried out across the six most affected regions 8–10 months post-floods. Data were collected on self-reported otolaryngology (ENT) and respiratory symptoms, along with primary and secondary stressors. Three levels of exposure were examined: flooded, disrupted and unaffected. Results: One in ten respondents declared being flooded and 31.4% being disrupted by the floods. Flooded and disrupted participants reported significantly more ENT symptoms (adjusted odds ratio (aOR): 3.18; 95% CI: 2.45–4.14; aOR: 1.76; 95% CI: 1.45–2.14) and respiratory symptoms (aOR: 3.41; 95% CI: 2.45–4.75; aOR: 1.45; 95% CI: 1.10–1.91) than the unaffected participants. All primary stressors and certain secondary stressors assessed were significantly associated with both ENT and respiratory symptoms, but no “dose–response” gradient could be observed. Conclusion: This study highlights the long-term adverse effects of flood exposure on respiratory health.

Abstract The Maritime Continent is one of the most challenging regions for atmospheric models. Processes that modulate deep convection are poorly represented in models, which affects their ability to simulate precipitation features accurately. Thus, future projections of precipitation over the region are prone to large uncertainties. One of the key players in modeling tropical precipitation is the convective representation, and hence convection-permitting experiments have contributed to improve aspects of precipitation in models. This improvement creates opportunities to explore the physical processes that govern rainfall in the Maritime Continent, as well as their role in a warming climate. Here, we examine the response to climate change of models with explicit and parameterized convection and how that reflects in precipitation changes. We focus on the intensification of spatial contrasts as precursors of changes in mean and extreme precipitation in the tropical archipelago. Our results show that the broad picture is similar in both model setups, where islands will undergo an increase in mean and extreme precipitation in a warmer climate and the ocean will see less rain. However, the magnitude and spatial structure of such changes, as well as the projection of rainfall percentiles, are different across model experiments. We suggest that while the primary effect of climate change is thermodynamical and it is similarly reproduced by both model configurations, dynamical effects are represented quite differently in explicit and parameterized convection experiments. In this study, we link such differences to horizontal and vertical spatial contrasts and how convective representations translate them into precipitation changes.

Abstract This study investigates the seasonality of near‐surface wind speeds associated with extratropical cyclones (ETCs) over northeastern North America using a global reanalysis data set during 1979–2020. As opposed to most studies that emphasize winter storms, ETCs during the fall exhibit significantly stronger 10‐m winds over this region due to the slightly stronger continental cyclones and significantly weaker low‐level stability during that time of the year. Also, ETCs favor inland lakes and Hudson Bay during the low‐ice‐content fall season, leading to lower surface roughness. Combining these results, we derive simple linear regressions to predict the 10‐m wind speed given three variables: 850‐hPa wind speed, low‐level Richardson number, and surface roughness length. This formula captures the observed seasonality and serves as a valuable tool for cyclone near‐surface wind risk assessment. , Plain Language Summary Extratropical cyclones can bring powerful winds that can cause severe damage to infrastructure. We find that cyclones with severe winds are the most frequent in the fall season over continental northeastern North America. Three reasons are found responsible: stronger continental cyclones, weaker low‐level atmospheric stability, and the lower surface roughness over lakes and Hudson Bay, where cyclones frequently occur in fall. A simple formula that can effectively assess the near‐surface wind speeds associated with cyclones is derived based on these results. , Key Points Extratropical‐cyclone‐associated 10‐m wind speeds are the strongest in the fall season over northeastern North America Besides stronger continental cyclones and 850‐hPa winds, weaker low‐level stability in fall favors stronger 10‐m wind speeds in this region Linear regression using 850‐hPa wind, Richardson number, and surface roughness well predicts cyclones' 10‐m wind speeds and seasonality

Abstract A warmer climate impacts streamflows and these changes need to be quantified to assess future risk, vulnerability, and to implement efficient adaptation measures. The climate simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), which have been the basis of most such assessments over the past decade, are being gradually superseded by the more recent Coupled Model Intercomparison Project Phase 6 (CMIP6). Our study portrays the added value of the CMIP6 ensemble over CMIP5 in a first North America wide comparison using 3,107 catchments. Results show a reduced spread of the CMIP6 ensemble compared to the CMIP5 ensemble for temperature and precipitation projections. In terms of flow indicators, the CMIP6 driven hydrological projections result in a smaller spread of future mean and high flow values, except for mountainous areas. Overall, we assess that the CMIP6 ensemble provides a narrower band of uncertainty of future climate projections, bringing more confidence for hydrological impact studies. , Plain Language Summary Greenhouse gas emissions are causing the climate to warm significantly, which in turn impacts flows in rivers worldwide. To adapt to these changes, it is essential to quantify them and assess future risk and vulnerability. Climate models are the primary tools used to achieve this. The main data set that provides scientists with state‐of‐the‐art climate model simulations is known as the Coupled Model Intercomparison Project (CMIP). The fifth phase of that project (CMIP5) has been used over the past decade in multiple hydrological studies to assess the impacts of climate change on streamflow. The more recent sixth phase (CMIP6) has started to generate projections, which brings the following question: is it necessary to update the hydrological impact studies performed using CMIP5 with the new CMIP6 models? To answer this question, a comparison between CMIP5 and CMIP6 using 3,107 catchments over North America was conducted. Results show that there is less spread in temperature and precipitation projections for CMIP6. This translates into a smaller spread of future mean, high and low flow values, except for mountainous areas. Overall, we assess that using the CMIP6 data set would provide a more concerted range of future climate projections, leading to more confident hydrological impact studies. , Key Points A comparison of hydrological impacts using Coupled Model Intercomparison Project version 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) ensembles is performed over 3,107 catchments in North America The CMIP6 ensembles provide a narrower band of uncertainty for hydrological indicators in the future It is recommended to update hydrological impact studies performed using CMIP5 with the CMIP6 ensemble

Carbon allocation is an important mechanism through which plants respond to environmental changes. To enhance our understanding of maximizing carbon uptake by controlling planting densities, the carbon allocation module of a process-based model, TRIPLEX-Management, was modified and improved by introducing light, soil water, and soil nitrogen availability factors to quantify the allocation coefficients for different plant organs. The modified TRIPLEX-Management model simulation results were verified against observations from northern Jiangsu Province, China, and then the model was used to simulate dynamic changes in forest carbon under six density scenarios (200, 400, 600, 800, 1000, and 1200 stems ha−1). The mean absolute errors between the predicted and observed variables of the mean diameter at breast height, mean height, and estimated aboveground biomass ranged from 15.0% to 26.6%, and were lower compared with the original model simulated results, which ranged from 24.4% to 60.5%. The normalized root mean square errors ranged from 0.2 to 0.3, and were lower compared with the original model simulated results, which ranged from 0.3 to 0.6. The Willmott index between the predicted and observed variables also varied from 0.5 to 0.8, indicating that the modified TRIPLEX-Management model could accurately simulate the dynamic changes in poplar (Populus spp.) plantations with different densities in northern Jiangsu Province. The density scenario results showed that the leaf and fine root allocation coefficients decreased with the increase in stand density, while the stem allocation increased. Overall, our study showed that the optimum stand density (approximately 400 stems ha−1) could reach the highest aboveground biomass for poplar stands and soil organic carbon storage, leading to higher ecological functions related to carbon sequestration without sacrificing wood production in an economical way in northern Jiangsu Province. Therefore, reasonable density control with different soil and climate conditions should be recommended to maximize carbon sequestration.

Abstract Freezing rain events have caused severe socioeconomic and ecosystem impacts. An understanding of how these events may evolve as the Earth warms is necessary to adequately adapt infrastructure to these changes. We present an analysis of projected changes to freezing rain events over North America relative to the 1980–2009 recent past climate for the periods during which +2, +3, and +4°C of global warming is attained. We diagnose freezing rain using four precipitation‐type algorithms (Cantin and Bachand, Bourgouin, Ramer, and Baldwin) applied to four simulations of the fifth‐generation Canadian Regional Climate Model (CRCM5) driven by four global climate models (GCMs). We find that the choice of driving GCM strongly influences the spatial pattern of projected change. The choice of algorithm has a comparatively smaller impact, and primarily affects the magnitude but not the sign of projected change. We identify several regions where all simulations and algorithms agree on the sign of change, with increases projected over portions of western Canada and decreases over the central, eastern, and southern United States. However, we also find large regions of disagreement on the sign of change depending on driving GCM and even ensemble member of the same GCM, highlighting the importance of examining freezing rain events in a multi‐member ensemble of simulations driven by multiple GCMs to sufficiently account for uncertainty in projections of these hazardous events. , Plain Language Summary Freezing rain events, or ice storms, can have major impacts on electrical infrastructure, agriculture, and road and air travel. Despite these impacts, relatively little research has been done on how these events may change as the Earth warms. We therefore examine several climate model simulations to determine how the frequency of freezing rain may change at different levels of future global warming. We focus in particular on how sensitive the projected changes are to the method used to identify freezing rain in the model output, as well as to the choice of climate model used to produce the projections. We find strong agreement among methods and models on a decrease in freezing rain frequency over much of the United States (from Texas northeastward to Maine) and an increase in freezing rain frequency over portions of western Canada (Alberta, Saskatchewan, Manitoba). In many other areas, however, the different methods and simulations disagree on the direction of projected change. Our findings highlight the importance of using many different climate models, rather than single simulations, to paint a clearer picture of the level of certainty in projections of freezing rain in the context of global warming. , Key Points Freezing rain is projected to increase in frequency over portions of western and central Canada and decrease over most of the United States The sign of projected changes is not highly sensitive to the precipitation‐type algorithm used to diagnose freezing rain The choice of driving global climate model is a key source of uncertainty in both the sign and magnitude of projected changes

Atmospheric deposition of nitrogen (N) and phosphorus (P) far exceeding the pre-industrial levels have the potential to change carbon (C) dynamics in northern peatlands. However, the responses of soil C concentration and organo-chemical composition to different rates and durations of nutrient enrichment are still unclear. Here, we compared the short- (3 years) and long-term (10 years) effects of N and P fertilizations on the physicochemical properties of peat and porewater in a bog-fen complex in northern China. Our results showed that the short-term fertilization increased Sphagnum moss cover, while the expansion of vascular plants was observed owing to the long-term fertilization. The preserved soil C did not vary considerably after the short- and long-term fertilizations. The harsh soil conditions may impede the decomposition of organic matters by soil microorganisms during the short-term fertilization. For the long-term fertilization, the input of high-phenolic litters owing to vascular plant expansion likely exerted an important control on soil C dynamics. These processes constrained the variation in soil C concentrations when the addition rate and cumulative amount of external N and P increased, which will advance our understanding and prediction of the resilience of soil C storage to imbalanced nutrient enrichment of N and P in northern peatlands.

Atmospheric deposition of nitrogen (N) and phosphorus (P) far exceeding the pre-industrial levels have the potential to change carbon (C) dynamics in northern peatlands. However, the responses of soil C concentration and organo-chemical composition to different rates and durations of nutrient enrichment are still unclear. Here, we compared the short- (3 years) and long-term (10 years) effects of N and P fertilizations on the physicochemical properties of peat and porewater in a bog-fen complex in northern China. Our results showed that the short-term fertilization increased Sphagnum moss cover, while the expansion of vascular plants was observed owing to the long-term fertilization. The preserved soil C did not vary considerably after the short- and long-term fertilizations. The harsh soil conditions may impede the decomposition of organic matters by soil microorganisms during the short-term fertilization. For the long-term fertilization, the input of high-phenolic litters owing to vascular plant expansion likely exerted an important control on soil C dynamics. These processes constrained the variation in soil C concentrations when the addition rate and cumulative amount of external N and P increased, which will advance our understanding and prediction of the resilience of soil C storage to imbalanced nutrient enrichment of N and P in northern peatlands.

Abstract Studies have estimated the impact of the environment on malaria incidence although few have explored the differential impact due to malaria control interventions. Therefore, the objective of the study was to evaluate the effect of indoor residual spraying (IRS) on the relationship between malaria and environment (i.e. rainfall, temperatures, humidity, and vegetation) using data from a dynamic cohort of children from three sub-counties in Uganda. Environmental variables were extracted from remote sensing sources and averaged over different time periods. General linear mixed models were constructed for each sub-counties based on a log-binomial distribution. The influence of IRS was analysed by comparing marginal effects of environment in models adjusted and unadjusted for IRS. Great regional variability in the shape (linear and non-linear), direction, and magnitude of environmental associations with malaria risk were observed between sub-counties. IRS was significantly associated with malaria risk reduction (risk ratios vary from RR = 0.03, CI 95% [0.03–0.08] to RR = 0.35, CI95% [0.28–0.42]). Model adjustment for this intervention changed the magnitude and/or direction of environment-malaria associations, suggesting an interaction effect. This study evaluated the potential influence of IRS in the malaria-environment association and highlighted the necessity to control for interventions when they are performed to properly estimate the environmental influence on malaria. Local models are more informative to guide intervention program compared to national models.

This dataset contains output of yearly frequencies (hours) of freezing rain identified using four precipitation-type algorithms applied to output of the fifth-generation Canadian Regional Climate Model (CRCM5) run at Ouranos. Algorithms are applied to three-hourly output of eight simulations of four dynamically-downscaled global climate models (GCMs) on a 0.22° horizontal grid over the North American domain. Simulations for 1980-2005 are forced with observed greenhouse gas concentrations, with data for 2006-2099 using the RCP 8.5 greenhouse gas concentration trajectory. Each occurrence of freezing rain identified in the model output is multiplied by 3 for comparison with hourly observations. These data are associated with the article "A multi-algorithm analysis of projected changes to freezing rain over North America in an ensemble of regional climate model simulations" by McCray et al., submitted in 2022 to the Journal of Geophysical Research: Atmospheres.

Many hypotheses have been proposed to explain elevational species richness patterns; however, evaluating their importance remains a challenge, as mountains that are nested within different biogeographic regions have different environmental attributes. Here, we conducted a comparative study for trees, shrubs, herbs, and ferns along the same elevational gradient for 22 mountains worldwide, examining the performance of hypotheses of energy, tolerance, climatic variability, and spatial area to explain the elevational species richness patterns for each plant group. Results show that for trees and shrubs, energy-related factors exhibit greater explanatory power than other factors, whereas the factors that are associated with climatic variability performed better in explaining the elevational species richness patterns of herbs and ferns. For colder mountains, energy-related factors emerged as the main drivers of woody species diversity, whereas in hotter and wetter ecosystems, temperature and precipitation were the most important predictors of species richness along elevational gradients. For herbs and ferns, the variation in species richness was less than that of woody species. These findings provide important evidence concerning the generality of the energy theory for explaining the elevational species richness pattern of plants, highlighting that the underlying mechanisms may change among different growth form groups and regions within which mountains are nested.

Many hypotheses have been proposed to explain elevational species richness patterns; however, evaluating their importance remains a challenge, as mountains that are nested within different biogeographic regions have different environmental attributes. Here, we conducted a comparative study for trees, shrubs, herbs, and ferns along the same elevational gradient for 22 mountains worldwide, examining the performance of hypotheses of energy, tolerance, climatic variability, and spatial area to explain the elevational species richness patterns for each plant group. Results show that for trees and shrubs, energy-related factors exhibit greater explanatory power than other factors, whereas the factors that are associated with climatic variability performed better in explaining the elevational species richness patterns of herbs and ferns. For colder mountains, energy-related factors emerged as the main drivers of woody species diversity, whereas in hotter and wetter ecosystems, temperature and precipitation were the most important predictors of species richness along elevational gradients. For herbs and ferns, the variation in species richness was less than that of woody species. These findings provide important evidence concerning the generality of the energy theory for explaining the elevational species richness pattern of plants, highlighting that the underlying mechanisms may change among different growth form groups and regions within which mountains are nested.

Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.

Phosphorus (P) is a key and a limiting nutrient in ecosystems and plays an important role in many physiological and biochemical processes, affecting both terrestrial ecosystem productivity and soil carbon storage. However, only a few global land surface models have incorporated P cycle and used to investigate the interactions of C-N-P and its limitation on terrestrial ecosystems. The overall objective of this study was to integrate the P cycle and its interaction with carbon (C) and nitrogen (N) into new processes model of TRIPLEX-CNP. In this study, key processes of the P cycle, including P pool sizes and fluxes in plant, litter, and soil were integrated into a new model framework, TRIPLEX-CNP. We also added dynamic P:C ratios for different ecosystems. Based on sensitivity analysis results, we identified the phosphorus resorption coefficient of leaf (rpleaf) as the most influential parameter to gross primary productivity (GPP) and biomass, and determined optimal coefficients for different plant functional types (PFTs). TRIPLEX-CNP was calibrated with 49 sites and validated against 116 sites across eight biomes globally. The results suggested that TRIPLEX-CNP performed well on simulating the global GPP and soil organic carbon (SOC) with respective R2 values of 0.85 and 0.78 (both p < 0.01) between simulated and observed values. The R2 of simulation and observation of total biomass are 0.67 (p < 0.01) by TRIPLEX-CNP. The overall model performance had been improved in global GPP, total biomass and SOC after adding the P cycle comparing with the earlier version. Our work represents the promising step toward new coupled ecosystem process models for improving the quantifications of land carbon cycle and reducing uncertainty.