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Wildfire impacts the global carbon cycle, property, harvestable timber, and public health. Canada saw a record fire season in 2023 with 14.9 Mha burned—over seven times the 1986–2022 average of 2.1 Mha. Here we utilize a new process-based wildfire module that explicitly represents fire weather, fuel type and availability, ignition sources, fire suppression, and vegetation's climate response to project the future of wildfire in Canada. Under rapid climate change (shared socioeconomic pathway [SSP] 370 & 585) simulated annual burned area in the 2090 s reaches 10.2 ± 2.1 to 11.7 ± 2.4 Mha, approaching the 2023 fire season total. However, climate change below a 2 °C global target (SSP126), keeps the 2090 s area burned near modern (2004–2014) norms. The simulated area burned and carbon emissions are most sensitive to climate drivers and lightning but future lightning activity is a key uncertainty. [ABSTRACT FROM AUTHOR]

The terrestrial biosphere plays a major role in the global carbon cycle, and there is a recognized need for regularly updated estimates of land‐atmosphere exchange at regional and global scales. An international ensemble of Dynamic Global Vegetation Models (DGVMs), known as the "Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide" (TRENDY) project, quantifies land biophysical exchange processes and biogeochemistry cycles in support of the annual Global Carbon Budget assessments and the REgional Carbon Cycle Assessment and Processes, phase 2 project. DGVMs use a common protocol and set of driving data sets. A set of factorial simulations allows attribution of spatio‐temporal changes in land surface processes to three primary global change drivers: changes in atmospheric CO2, climate change and variability, and Land Use and Land Cover Changes (LULCC). Here, we describe the TRENDY project, benchmark DGVM performance using remote‐sensing and other observational data, and present results for the contemporary period. Simulation results show a large global carbon sink in natural vegetation over 2012–2021, attributed to the CO2 fertilization effect (3.8 ± 0.8 PgC/yr) and climate (−0.58 ± 0.54 PgC/yr). Forests and semi‐arid ecosystems contribute approximately equally to the mean and trend in the natural land sink, and semi‐arid ecosystems continue to dominate interannual variability. The natural sink is offset by net emissions from LULCC (−1.6 ± 0.5 PgC/yr), with a net land sink of 1.7 ± 0.6 PgC/yr. Despite the largest gross fluxes being in the tropics, the largest net land‐atmosphere exchange is simulated in the extratropical regions. Plain Language Summary: Around one third of human‐induced CO2 emissions are absorbed by land ecosystems and thus act to mitigate climate change. It is essential to understand the processes, ecosystems and regions responsible for this natural carbon sink, to inform on the efficiency of the sinks into the future. These sinks are susceptible to year‐to‐year variation in response to climate variations and extremes. At the same time deforestation and other forms of land management are changing the land surface, which overall adds significantly to the human‐induced CO2 emissions. There is a need to regularly update our estimate of land carbon dynamics to aid global stock takes for the Paris agreement to avoid dangerous climate change. Here we present an international initiative that on an annual basis assesses "Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide" (TRENDY) using computer models of the land carbon cycle. We quantify the land sink during the contemporary period (2012–2021), and attribute to processes, mainly the large opposing effects of CO2 fertilization enhancing plant productivity and land‐use change. Forests and semi‐arid ecosystems are largely responsible for the mean and trend in the land sink, with the latter most important for its year‐to‐year variation. Key Points: We quantify and attribute land carbon dynamics to underlying processes at regional scales, contributing bottom‐up estimates to RECCAP‐2Models simulate a contemporary net land sink of 1.7 ± 0.6 PgC/yr, with large opposing effects of CO2 fertilization and land‐use changeDespite the largest gross fluxes being in the tropics, the largest net land‐atmosphere exchange is simulated in the extratropical regions [ABSTRACT FROM AUTHOR]

The effect of climate change is investigated on the hydro-climatology of six major Canadian rivers (Mackenzie, Yukon, Columbia, Fraser, Nelson, and St. Lawrence), in particular streamflow, by analyzing results from the historical and future simulations (RCP 4.5 and 8.5 scenarios) performed with the Canadian regional climate model (CanRCM4). Streamflow is obtained by routing runoff using river networks at 0.5° resolution. Of these six rivers, Nelson and St. Lawrence are the most regulated. As a result, the streamflow at the mouth of these rivers shows very little seasonality. Additionally, the Great Lakes significantly dampen the seasonality of streamflow for the St. Lawrence River. Mean annual precipitation (P), evaporation (E), runoff (R), and temperature increase for all six river basins considered and the increases are higher for the more fossil fuel-intensive RCP 8.5 scenario. The only exception is the Nelson River basin for which the simulated runoff increases are extremely small. The hydrological response of these rivers to climate warming is characterized by their existing climate states. The northerly Mackenzie and Yukon River basins show a decrease in evaporation ratio (E/P) and an increase in runoff ratio (R/P) since the increase in precipitation is more than enough to offset the increase in evaporation associated with increasing temperature. For the southerly Fraser and Columbia River basins, the E/P ratio increases, and the R/P ratio decreases due to an already milder climate in the Pacific north-western region. The seasonality of simulated monthly streamflow is also more affected for the southerly Fraser and Columbia Rivers than for the northerly Mackenzie and Yukon Rivers as snow amounts decrease and snowmelt occurs earlier. The streamflow seasonality for the Mackenzie and Yukon rivers is still dominated by snowmelt at the end of the century even in the RCP 8.5 scenario. The simulated streamflow regime for the Fraser and Columbia Rivers shifts from a snow-dominated to a hybrid/rainfall-dominated regime towards the end of this century in the RCP 8.5 scenario. While we expect the climate change signal from CanRCM4 to be higher than other climate models, owing to the higher-than-average climate sensitivity of its parent global climate model, the results presented here provide a consistent overview of hydrological changes across six major Canadian river basins in response to a warmer climate. [ABSTRACT FROM AUTHOR]

Earth System Models (ESMs) project that the terrestrial carbon sink will continue to grow as atmospheric CO2 increases, but this projection is uncertain due to biases in the simulated climate and how ESMs represent ecosystem processes. In particular, the strength of the CO2‐fertilization effect, which is modulated by nutrient cycles, varies substantially across models. This study evaluates land carbon balance uncertainties for the Canadian Earth System Model (CanESM) by conducting simulations where the latest version of CanESM's land surface component is driven offline with raw and bias‐adjusted CanESM5 climate forcing data. To quantify the impact of nutrient limitation, we complete simulations where the nitrogen cycle is enabled or disabled. Results show that bias adjustment improves model performance across most ecosystem variables, primarily due to reduced biases in precipitation. Turning the nitrogen cycle on increases the global land carbon sink during the historical period (1995–2014) due to enhanced nitrogen deposition, placing it within the Global Carbon Budget uncertainty range. During the future period (2080–2099), the simulated land carbon sink increases in response to bias adjustment and decreases in response to the dynamic carbon‐nitrogen interaction, leading to a net decrease when both factors are acting together. The dominating impact of the nitrogen cycle demonstrates the importance of representing nutrient limitation in ESMs. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement. Plain Language Summary: The implementation of global climate change policies relies on our ability to predict how the global carbon cycle will evolve in the future. Climate models project that the biosphere will continue to absorb more CO2 than it emits, keeping atmospheric CO2 levels lower than they would be otherwise. However, the strength of this net CO2 uptake varies considerably among models. This is because of differences in the simulated climate as well as the use of different methods for simulating plant growth. This study evaluates the importance of both factors by running one model with different climate data sets and model configurations. Our results show that the future net CO2 uptake by plants increases when removing biases in climatic conditions and decreases when accounting for the impact of soil nutrients on plant growth, leading to a net decrease when both factors are acting together. The dominating impact of the nutrients demonstrates the importance of representing nutrient limitation in climate models. Such efforts may produce more robust carbon balance projections in support of global climate change mitigation policies such as the 2015 Paris Agreement. Key Points: Bias adjustment of climate forcing improves model performance across most variables, primarily due to reduced biases in precipitationThe inclusion of the N cycle increases the simulated C sink during the historical period, placing it within the observed uncertainty rangeThe future C sink increases with bias adjustment and decreases with the N cycle, resulting in a net decrease when both factors are at play [ABSTRACT FROM AUTHOR]

Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process. [ABSTRACT FROM AUTHOR]

Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Large differences are found in the geographical distribution of PFTs currently used in various LSMs. These differences arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgment and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). A previous study has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFTs and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process. [ABSTRACT FROM AUTHOR]

A terrestrial nitrogen (N) cycle model is coupled to the carbon (C) cycle in the framework of the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC). CLASSIC currently models physical and biogeochemical processes and simulates fluxes of water, energy, and CO2 at the land–atmosphere boundary. CLASSIC is similar to most models and its gross primary productivity increases in response to increasing atmospheric CO2 concentration. In the current model version, a downregulation parameterization emulates the effect of nutrient constraints and scales down potential photosynthesis rates, using a globally constant scalar, as a function of increasing CO2. In the new model when nitrogen (N) and carbon (C) cycles are coupled, cycling of N through the coupled soil–vegetation system facilitates the simulation of leaf N amount and maximum carboxylation capacity (Vcmax) prognostically. An increase in atmospheric CO2 decreases leaf N amount and therefore Vcmax , allowing the simulation of photosynthesis downregulation as a function of N supply. All primary N cycle processes that represent the coupled soil–vegetation system are modelled explicitly. These include biological N fixation; treatment of externally specified N deposition and fertilization application; uptake of N by plants; transfer of N to litter via litterfall; mineralization; immobilization; nitrification; denitrification; ammonia volatilization; leaching; and the gaseous fluxes of NO, N2O , and N2. The interactions between terrestrial C and N cycles are evaluated by perturbing the coupled soil–vegetation system in CLASSIC with one forcing at a time over the 1850–2017 historical period. These forcings include the increase in atmospheric CO2 , change in climate, increase in N deposition, and increasing crop area and fertilizer input, over the historical period. An increase in atmospheric CO2 increases the C:N ratio of vegetation; climate warming over the historical period increases N mineralization and leads to a decrease in the vegetation C:N ratio; N deposition also decreases the vegetation C:N ratio. Finally, fertilizer input increases leaching, NH 3 volatilization, and gaseous losses of N 2 , N 2 O, and NO. These model responses are consistent with conceptual understanding of the coupled C and N cycles. The simulated terrestrial carbon sink over the 1959–2017 period, from the simulation with all forcings, is 2.0 PgCyr-1 and compares reasonably well with the quasi observation-based estimate from the 2019 Global Carbon Project (2.1 PgCyr-1). The contribution of increasing CO2 , climate change, and N deposition to carbon uptake by land over the historical period (1850–2017) is calculated to be 84 % , 2 % , and 14 % , respectively. [ABSTRACT FROM AUTHOR]

Results from the fully and biogeochemically coupled simulations in which CO2 increases at a rate of 1 % yr -1 (1pctCO2) from its preindustrial value are analyzed to quantify the magnitude of carbon–concentration and carbon–climate feedback parameters which measure the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate, respectively. The results are based on 11 comprehensive Earth system models from the most recent (sixth) Coupled Model Intercomparison Project (CMIP6) and compared with eight models from the fifth CMIP (CMIP5). The strength of the carbon–concentration feedback is of comparable magnitudes over land (mean ± standard deviation = 0.97 ± 0.40 PgC ppm -1) and ocean (0.79 ± 0.07 PgC ppm -1), while the carbon–climate feedback over land (-45.1 ± 50.6 PgC ∘ C -1) is about 3 times larger than over ocean (-17.2 ± 5.0 PgC ∘ C -1). The strength of both feedbacks is an order of magnitude more uncertain over land than over ocean as has been seen in existing studies. These values and their spread from 11 CMIP6 models have not changed significantly compared to CMIP5 models. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The transient climate response to cumulative emissions (TCRE) from the 11 CMIP6 models considered here is 1.77 ± 0.37 ∘ C EgC -1 and is similar to that found in CMIP5 models (1.63 ± 0.48 ∘ C EgC -1) but with somewhat reduced model spread. The expressions for feedback parameters based on the fully and biogeochemically coupled configurations of the 1pctCO2 simulation are simplified when the small temperature change in the biogeochemically coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters is used to gain insight into the reasons for differing responses among ocean and land carbon cycle models. [ABSTRACT FROM AUTHOR]

Changing amplitude of the seasonal cycle of atmospheric CO2 (SCA) in the northern hemisphere is an emerging carbon cycle property. Mauna Loa (MLO) station (20°N, 156°W), which has the longest continuous northern hemisphere CO2 record, shows an increasing SCA before the 1980s (p <.01), followed by no significant change thereafter. We analyzed the potential driving factors of SCA slowing‐down, with an ensemble of dynamic global vegetation models (DGVMs) coupled with an atmospheric transport model. We found that slowing‐down of SCA at MLO is primarily explained by response of net biome productivity (NBP) to climate change, and by changes in atmospheric circulations. Through NBP, climate change increases SCA at MLO before the 1980s and decreases it afterwards. The effect of climate change on the slowing‐down of SCA at MLO is mainly exerted by intensified drought stress acting to offset the acceleration driven by CO2 fertilization. This challenges the view that CO2 fertilization is the dominant cause of emergent SCA trends at northern sites south of 40°N. The contribution of agricultural intensification on the deceleration of SCA at MLO was elusive according to land–atmosphere CO2 flux estimated by DGVMs and atmospheric inversions. Our results also show the necessity to adequately account for changing circulation patterns in understanding carbon cycle dynamics observed from atmospheric observations and in using these observations to benchmark DGVMs. [ABSTRACT FROM AUTHOR]

Changes in forest cover have a strong effect on climate through the alteration of surface biogeophysical and biogeochemical properties that affect energy, water, and carbon exchange with the atmosphere. To quantify biogeophysical and biogeochemical effects of deforestation in a consistent setup, nine Earth System models carried out an idealized experiment in the framework of the Coupled Model Intercomparison Project, phase 6 (CMIP6). Starting from their pre- industrial state, models linearly replace 20 million km2 of tree area in densely forested regions with grasslands over a period of 50 years followed by a stabilization period of 30 years. Most of the deforested area is in tropics, with a secondary peak in the boreal region. This study compares the effect of this large deforestation perturbation on energy and carbon fluxes across models. The effect on global annual near-surface temperature ranges from no significant change to a cooling by 0.55 °C, with a multi-model mean of -0.22 ± 0.21 °C. Five models simulate a temperature increase over deforested land in the tropics and a cooling over deforested boreal land. In these models, the latitude at which the temperature response changes sign ranges from 11 to 43° N, with a multi-model mean of 23° N. A multi-ensemble analysis reveals that the near-surface temperature changes emerge within 50 years over the tropical regions propagating from the centre of deforestation to the edges, indicating the influence of non-local effects. The biogeochemical effect of deforestation are land carbon losses of 259 ± 80 PgC. Based on transient climate response to cumulative emissions (TCRE) this would yield a warming by 0.46 ± 0.22 °C, suggesting a net warming effect of deforestation. While there is general agreement across models in their response to deforestation in terms of change in global temperatures and land carbon pools, the underlying changes in energy and carbon fluxes diverge substantially across models and geographical regions. Future analyses of the global deforestation experiments could further explore the effect on changes in seasonality of the climate response as well as large-scale circulation changes to advance our understanding and quantification of deforestation effects in the ESM frameworks. [ABSTRACT FROM AUTHOR]

Recent reports by the Global Carbon Project highlight large uncertainties around land surface processes such as land use change, strength of CO2 fertilization, nutrient limitation and supply, and response to variability in climate. Process-based land surface models are well suited to address these complex and emerging global change problems but will require extensive development and evaluation. The coupled Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) framework has been under continuous development by Environment and Climate Change Canada since 1987. As the open-source model of code development has revolutionized the software industry, scientific software is experiencing a similar evolution. Given the scale of the challenge facing land surface modellers, and the benefits of open-source, or community model, development, we have transitioned CLASS-CTEM from an internally developed model to an open-source community model, which we call the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC) v.1.0. CLASSIC contains many technical features specifically designed to encourage community use including software containerization for serial and parallel simulations, extensive benchmarking software and data (Automated Model Benchmarking; AMBER), self-documenting code, community standard formats for model inputs and outputs, amongst others. Here, we evaluate and benchmark CLASSIC against 31 FLUXNET sites where the model has been tailored to the site-level conditions and driven with observed meteorology. Future versions of CLASSIC will be developed using AMBER and these initial benchmark results to evaluate model performance over time. CLASSIC remains under active development and the code, site-level benchmarking data, software container, and AMBER are freely available for community use. [ABSTRACT FROM AUTHOR]

A terrestrial nitrogen (N) cycle model is coupled to carbon (C) cycle in the framework of the Canadian Land Surface Scheme Including biogeochemical Cycles (CLASSIC). CLASSIC currently models physical and biogeochemical processes and simulates fluxes of water, energy, and CO2 at the land-atmosphere boundary. Similar to most models, gross primary productivity in CLASSIC increases in response to increasing atmospheric CO2 concentration. In the current model version, a downregulation parameterization emulates the effect of nutrient constraints and scales down potential photosynthesis rates, using a globally constant scalar, as a function of increasing CO2. In the new model when N and C cycles are coupled, cycling of N through the coupled soil-vegetation system facilitates the simulation of leaf N content and maximum carboxylation capacity (Vcmax) prognostically. An increase in atmospheric CO2 decreases leaf N content, and therefore Vcmax, allowing the simulation of photosynthesis downregulation as a function of N supply. All primary N cycle processes, that represent the coupled soil-vegetation system, are modelled explicitly. These include biological N fixation, treatment of externally specified N deposition and fertilization application, uptake of N by plants, transfer of N to litter via litterfall, mineralization, immobilization, nitrification, ammonia volatilization, leaching, and the gaseous fluxes of NO, N2O, and N2. The interactions between terrestrial C and N cycles are evaluated by perturbing the coupled soil vegetation system in CLASSIC with one forcing at a time over the 1850-2017 historical period. These forcings include the increase in atmospheric CO2, change in climate, increase in N deposition, and increasing crop area and fertilizer input, over the historical period. The model response to these forcings is consistent with conceptual understanding of the coupled C and N cycles. The simulated terrestrial carbon sink over the 1959-2017 period, from the simulation with all forcings, is 2.0 Pg C/yr and compares reasonably well with the quasi observation-based estimate from the 2019 Global Carbon Project (2.1 Pg C/yr). The contribution of increasing CO2, climate change, and N deposition to carbon uptake by land over the historical period (1850-2017) is calculated to be 84%, 2%, and 14%, respectively. [ABSTRACT FROM AUTHOR]

Soil salinity has emerged as a serious issue for global food security. It is estimated that currently about 62 million hectares or 20 percent of the world's irrigated land is affected by salinity. The deposition of an excess amount of soluble salt in cultivable land directly affects crop yields. The uptake of high amount of salt inhibits diverse physiological and metabolic processes of plants even impacting their survival. The conventional methods of reclamation of saline soil which involve scraping, flushing, leaching or adding an amendment (e.g., gypsum, CaCl2, etc.) are of limited success and also adversely affect the agro-ecosystems. In this context, developing sustainable methods which increase the productivity of saline soil without harming the environment are necessary. Since long, breeding of salt-tolerant plants and development of salt-resistant crop varieties have also been tried, but these and aforesaid conventional approaches are not able to solve the problem. Salt tolerance and dependence are the characteristics of some microbes. Salt-tolerant microbes can survive in osmotic and ionic stress. Various genera of salt-tolerant plant growth promoting rhizobacteria (ST-PGPR) have been isolated from extreme alkaline, saline, and sodic soils. Many of them are also known to mitigate various biotic and abiotic stresses in plants. In the last few years, potential PGPR enhancing the productivity of plants facing salt-stress have been researched upon suggesting that ST-PGPR can be exploited for the reclamation of saline agro-ecosystems. In this review, ST-PGPR and their potential in enhancing the productivity of saline agro-ecosystems will be discussed. Apart from this, PGPR mediated mechanisms of salt tolerance in different crop plants and future research trends of using ST-PGPR for reclamation of saline soils will also be highlighted. [ABSTRACT FROM AUTHOR]

Results from the fully-, biogeochemically-, and radiatively-coupled simulations in which CO2 increases at a rate of 1% per year (1pctCO2) from its pre-industrial value are analyzed to quantify the magnitude of two feedback parameters which characterize the coupled carbon-climate system. These feedback parameters quantify the response of ocean and terrestrial carbon pools to changes in atmospheric CO2 concentration and the resulting change in global climate. The results are based on eight comprehensive Earth system models from the fifth Coupled Model Intercomparison Project (CMIP5) and eleven models from the sixth CMIP (CMIP6). The comparison of model results from two CMIP phases shows that, for both land and ocean, the model mean values of the feedback parameters and their multi-model spread has not changed significantly across the two CMIP phases. The absolute values of feedback parameters are lower for land with models that include a representation of nitrogen cycle. The sensitivity of feedback parameters to the three different ways in which they may be calculated is shown and, consistent with existing studies, the most relevant definition is that calculated using results from the fully- and biogeochemically-coupled configurations. Based on these two simulations simplified expressions for the feedback parameters are obtained when the small temperature change in the biogeochemically-coupled simulation is ignored. Decomposition of the terms of these simplified expressions for the feedback parameters allows identification of the reasons for differing responses among ocean and land carbon cycle models. [ABSTRACT FROM AUTHOR]

On regional to global scales, few constraints exist on gross primary productivity (GPP) and ecosystem respiration (Re) fluxes. Yet constraints on these fluxes are critical for evaluating and improving terrestrial biosphere models. In this study, we evaluate the seasonal cycle of GPP, Re, and net ecosystem exchange (NEE) produced by four terrestrial biosphere models and FLUXCOM, a data‐driven model, over northern midlatitude ecosystems. We evaluate the seasonal cycle of GPP and NEE using solar‐induced fluorescence retrieved from the Global Ozone Monitoring Experiment‐2 and column‐averaged dry‐air mole fractions of CO2 (XCO2) from the Total Carbon Column Observing Network, respectively. We then infer Re by combining constraints on GPP with constraints on NEE from two flux inversions. An ensemble of optimized Re seasonal cycles is generated using five GPP estimates and two NEE estimates. The optimized Re curves generally show high consistency with each other, with the largest differences due to the magnitude of GPP. We find optimized Re exhibits a systematically broader summer maximum than modeled Re, with values lower during June–July and higher during the fall than Re. Further analysis suggests that the differences could be due to seasonal variations in the carbon use efficiency (possibly due to an ecosystem‐scale Kok effect) and to seasonal variations in the leaf litter and fine root carbon pool. The results suggest that the inclusion of variable carbon use efficiency for autotrophic respiration and carbon pool dependence for heterotrophic respiration is important for accurately simulating Re. Key Points: Top‐down constraints on ecosystem respiration are obtained by combining atmospheric CO2 and solar‐induced fluorescence observationsInferred ecosystem respiration suggests a systematically broader summer maximum than bottom‐up estimates over the northern midlatitudesInferred ecosystem respiration shows high sensitivity to the magnitude of gross primary productivity [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and its seasonal dynamics are key determinants of vegetation productivity in nature and as represented in terrestrial biosphere models seeking to understand land-surface atmosphere flux dynamics and its response to climate change. Non-structural carbohydrates (NSCs) and their seasonal variability are known to play a crucial role in seasonal variation of leaf phenology and growth and functioning of plants. The carbon stored in NSC pools provides a buffer during times when supply and demand of carbon are asynchronous. An example of this role is illustrated when NSCs from previous years are used to initiate leaf onset at the arrival of favourable weather conditions. In this study, we incorporate NSC pools and associated parameterizations of new processes in the modelling framework of the Canadian Land Surface Scheme-Canadian Terrestrial Ecosystem Model (CLASS-CTEM) with an aim to improve the seasonality of simulated LAI. The performance of these new parameterizations is evaluated by comparing simulated LAI and atmosphere-land CO2 fluxes, to their observation-based estimates, at three sites characterized by broadleaf cold deciduous trees selected from the Fluxnet database. Results show an improvement in leaf onset and offset times with about 2 weeks shift towards earlier times during the year in better agreement with observations. These improvements in simulated LAI help to improve the simulated seasonal cycle of gross primary productivity (GPP) and as a result simulated net ecosystem productivity (NEP) as well. [ABSTRACT FROM AUTHOR]

The Canadian Land Surface Scheme and the Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component in the family of Canadian Earth system models (CanESMs). Here, CLASS-CTEM is coupled to Environment and Climate Change Canada (ECCC)'s weather and greenhouse gas forecast model (GEM-MACH-GHG) to consistently model atmosphere-land exchange of CO2. The coupling between the land and the atmospheric transport model ensures consistency between meteorological forcing of CO2 fluxes and CO2 transport. The procedure used to spin up carbon pools for CLASSCTEM for multi-decadal simulations needed to be significantly altered to deal with the limited availability of consistent meteorological information from a constantly changing operational environment in the GEM-MACH-GHG model. Despite the limitations in the spin-up procedure, the simulated fluxes obtained by driving the CLASS-CTEM model with meteorological forcing from GEM-MACH-GHG were comparable to those obtained from CLASS-CTEM when it is driven with standard meteorological forcing from the Climate Research Unit (CRU) combined with reanalysis fields from the National Centers for Environmental Prediction (NCEP) to form CRU-NCEP dataset. This is due to the similarity of the two meteorological datasets in terms of temperature and radiation. However, notable discrepancies in the seasonal variation and spatial patterns of precipitation estimates, especially in the tropics, were reflected in the estimated carbon fluxes, as they significantly affected the magnitude of the vegetation productivity and, to a lesser extent, the seasonal variations in carbon fluxes. Nevertheless, the simulated fluxes based on the meteorological forcing from the GEM-MACH-GHG model are consistent to some extent with other estimates from bottom-up or top-down approaches. Indeed, when simulated fluxes obtained by driving the CLASS-CTEM model with meteorological data from the GEM-MACH-GHG model are used as prior estimates for an atmospheric CO2 inversion analysis using the adjoint of the GEOS-Chem model, the retrieved CO2 flux estimates are comparable to those obtained from other systems in terms of the global budget and the total flux estimates for the northern extratropical regions, which have good observational coverage. In data-poor regions, as expected, differences in the retrieved fluxes due to the prior fluxes become apparent. Coupling CLASS-CTEM into the Environment Canada Carbon Assimilation System (EC-CAS) is considered an important step toward understanding how meteorological uncertainties affect both CO2 flux estimates and modeled atmospheric transport. Ultimately, such an approach will provide more direct feedback to the CLASS-CTEM developers and thus help to improve the performance of CLASS-CTEM by identifying the model limitations based on atmospheric constraints. [ABSTRACT FROM AUTHOR]

The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (~biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (T-D, exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories, and data-driven approaches (B-U, including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by T-D inversions at 558 & Tg & CH4 & yr-1 (range [540-568]). About 60 & % of global emissions are anthropogenic (range [50-65 & %]). B-U approaches suggest larger global emissions (736 & Tg & CH4 & yr-1 [596-884]) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the T-D budget, it is likely that some of the individual emissions reported by the B-U approaches are overestimated, leading to too large global emissions. Latitudinal data from T-D emissions indicate a predominance of tropical emissions (~64 & % of the global budget, < & 30° & N) as compared to mid (~32 & %, 30° & N-60° & N) and high northern latitudes (~ & 4 & %, 60° & N-90° & N). T-D inversions consistently infer lower emissions in China (~58 & Tg & CH4 & yr-1 [51-72], -14 & %) and higher emissions in Africa (86 & Tg & CH4 & yr-1 [73-108], +19 & %) than B-U values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for T-D inversions than for B-U inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute for 30-40 & % on the estimated range for wetland emissions. Other priorities for improving the methane budget include: i) the development of process-based models for inland-water emissions, ii) the intensification of methane observations at local scale (flux measurements) to constrain B-U land surface models, and at regional scale (surface networks and satellites) to constrain T-D inversions, iii) improvements in the estimation of atmospheric loss by OH, and iv) improvements of the transport models integrated in T-D inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (doi:10.3334/CDIAC/Global_Methane_Budget_2016). [ABSTRACT FROM AUTHOR]

The Canadian Terrestrial Ecosystem Model (CTEM) is the interactive vegetation component in the Earth system model of the Canadian Centre for Climate Modelling and Analysis. CTEM models land-atmosphere exchange of CO2 through the response of carbon in living vegetation, and dead litter and soil pools, to changes in weather and climate at timescales of days to centuries. Version 1.0 of CTEM uses prescribed fractional coverage of plant functional types (PFTs) although, in reality, vegetation cover continually adapts to changes in climate, atmospheric composition, and anthropogenic forcing. Changes in the spatial distribution of vegetation occur on timescales of years to centuries as vegetation distributions inherently have inertia. Here, we present version 2.0 of CTEM which includes a representation of competition between PFTs based on a modified version of the Lotka-Volterra (L-V) predator-prey equations. Our approach is used to dynamically simulate the fractional coverage of CTEM's seven natural, non-crop PFTs which are then compared with available observation-based estimates. Results from CTEM v. 2.0 show the model is able to represent the broad spatial distributions of its seven PFTs at the global scale. However, differences remain between modelled and observation-based fractional coverages of PFTs since representing the multitude of plant species globally, with just seven non-crop PFTs, only captures the large scale climatic controls on PFT distributions. As expected, PFTs that exist in climate niches are difficult to represent either due to the coarse spatial resolution of the model, and the corresponding driving climate, or the limited number of PFTs used. We also simulate the fractional coverages of PFTs using unmodified L-V equations to illustrate its limitations. The geographic and zonal distributions of primary terrestrial carbon pools and fluxes from the versions of CTEM that use prescribed and dynamically simulated fractional coverage of PFTs compare reasonably well with each other and observation-based estimates. The parametrization of competition between PFTs in CTEM v. 2.0 based on the modified L-V equations behaves in a reasonably realistic manner and yields a tool with which to investigate the changes in spatial distribution of vegetation in response to future changes in climate. [ABSTRACT FROM AUTHOR]

The response of the terrestrial carbon cycle to future changes in climate and atmospheric CO2 is assessed by analysing simulation results for the 2006-2100 period made with the second generation Canadian Earth system model (CanESM2) for the RCP 2.6, RCP 4.5 and RCP 8.5 climate change scenarios. Our interest is in the extent to which global terrestrial carbon pools and sinks, in particular those of the Amazonian region, are vulnerable to the adverse effects of climate change. CanESM2 results indicate that land remains an overall sink of atmospheric carbon for the 2006-2100 period. The net carbon uptake by land in response to changes in climate and atmospheric CO2 is close to 20, 80 and 140 Pg C for the RCP 2.6, 4.5 and 8.5 scenarios, respectively. The latitudinal structure of future atmosphere-land CO2 flux remains similar to that observed for the historical period with northern mid- to high-latitude regions gaining carbon from the atmosphere while the tropics remain either carbon neutral or a modest source of atmospheric carbon depending on scenario. These changes occur in conjunction with simulated precipitation and soil moisture increases over northern mid- and high-latitude land regions and precipitation and soil moisture decreases over the South American continent in all scenarios. Compared to other regions of the globe, which are either carbon sinks or near neutral, the Amazonian region is simulated to be a net source of carbon during the 21st century. Moreover, and unexpectedly, the rate of carbon loss to the atmosphere from the Amazonian region is largely independent of the differences between the three scenarios considered. [ABSTRACT FROM AUTHOR]

Dynamic vegetation models provide the ability to simulate terrestrial carbon pools and fluxes and a useful tool to study how these are affected by climate variability and climate change. At the continental scale, the spatial distribution of climate, in particular temperature and precipitation, strongly determines surface vegetation characteristics. Model validation exercises typically consist of driving a model with observation-based climate data and then comparing simulated quantities with their observation-based counterparts. However, observation-based datasets themselves may not necessarily be consistent with each other. Here, we compare simulated terrestrial carbon pools and fluxes over North America with observation-based estimates. Simulations are performed using the dynamic vegetation model Canadian Terrestrial Ecosystem Model (CTEM) coupled to the Canadian Land Surface Scheme (CLASS) when driven with two reanalysis-based climate datasets. The driving ECMWF reanalysis data ( ERA40) and NCEP/ NCAR reanalysis I data (NCEP) show differences when compared to each other, as well as when compared to the observation-based climate research unit (CRU) data. Most simulated carbon pools and fluxes show important differences, particularly over eastern North America, primarily due to differences in precipitation and temperature in the two reanalysis. However, despite very different gross fluxes, the model yields fairly similar estimates of the net atmosphere-land CO2 flux when driven with the two forcing datasets. The ERA40 driven simulation produces terrestrial pools and fluxes that compare better with observation-based estimates. These simulations do not take into account land use change or nitrogen deposition, both of which have been shown to enhance the land carbon sink over North America. The simulated sink of 0.5 Pg C year−1 during the 1980s and 1990s is therefore lower than inversion-based estimates. The analysis of spatial distribution of trends in simulated carbon pools and fluxes shows that the simulated carbon sink is driven primarily by NPP enhancements over eastern United States. © 2013 The Authors and Her Majesty the Queen in Right of Canada. International Journal of Climatology published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

The impacts of climate change and increasing atmospheric CO2 concentration on the terrestrial uptake of carbon dioxide since 1860 in the Canadian province of British Columbia are estimated using the process-based Canadian Terrestrial Ecosystem Model (CTEM). Model simulations show that these two factors yield an enhanced carbon uptake of around 44 gCm-2 yr-1 (or equivalently 63 gCm-2 yr-1 over the province's forested area), during the 1980s and 1990s, and continuing into the 2000s. About three-quarters of the simulated sink enhancement in our study compared to pre-industrial conditions is attributed to changing climate, and the rest is attributed to increase in CO2 concentration. The model response to changing climate and increasing CO2 is corroborated by comparing simulated stem wood growth rates with ground-based measurements from inventory plots in coastal British Columbia. The simulated sink is not an estimate of the net carbon balance because the effects of harvesting, insect disturbances and land-use change are not considered. [ABSTRACT FROM AUTHOR]

The ratio of warming to cumulative emissions of carbon dioxide has been shown to be approximately independent of time and emissions scenarios and directly relates emissions to temperature. It is therefore a potentially important tool for climate mitigation policy. The transient climate response to cumulative carbon emissions (TCRE), defined as the ratio of global-mean warming to cumulative emissions at CO2 doubling in a 1% yr−1 CO2 increase experiment, ranges from 0.8 to 2.4 K EgC−1 in 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5)-a somewhat broader range than that found in a previous generation of carbon-climate models. Using newly available simulations and a new observational temperature dataset to 2010, TCRE is estimated from observations by dividing an observationally constrained estimate of CO2-attributable warming by an estimate of cumulative carbon emissions to date, yielding an observationally constrained 5%-95% range of 0.7-2.0 K EgC−1. [ABSTRACT FROM AUTHOR]

The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon-climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon-concentration and carbon-climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon-concentration feedback to diagnosed cumulative emissions that are consistent with the 1% increasing CO2 concentration scenario is about 4.5 times larger than the carbon-climate feedback. Differences in the modeled responses of the carbon budget to changes in CO2 and temperature are seen to be 3-4 times larger for the land components compared to the ocean components of participating models. The feedback parameters depend on the state of the system as well the forcing scenario but nevertheless provide insight into the behavior of the coupled carbon-climate system and a useful common framework for comparing models. [ABSTRACT FROM AUTHOR]

Leaf phenology remains one of the most difficult processes to parameterize in terrestrial ecosystem models because our understanding of the physical processes that initiate leaf onset and senescence is incomplete. While progress has been made at the molecular level, for example by identifying genes that are associated with senescence and flowering for selected plant species, a picture of the processes controlling leaf phenology is only beginning to emerge. A variety of empirical formulations have been used with varying degrees of success in terrestrial ecosystem models for both extratropical and tropical biomes. For instance, the use of growing degree-days (GDDs) to initiate leaf onset has received considerable recognition and this approach is used in a number of models. There are, however, limitations when using GDDs and other empirically based formulations in global transient climate change simulations.The phenology scheme developed for the Canadian Terrestrial Ecosystem Model (CTEM), designed for inclusion in the Canadian Centre for Climate Modelling and Analysis coupled general circulation model, is described. The representation of leaf phenology is general enough to be applied over the globe and sufficiently robust for use in transient climate change simulations. Leaf phenology is functionally related to the (possibly changing) climate state and to atmospheric composition rather than to geographical boundaries or controls implicitly based on current climate. In this approach, phenology is controlled by environmental conditions as they affect the carbon balance. A carbon-gain-based scheme initiates leaf onset when it is beneficial for the plant, in carbon terms, to produce new leaves. Leaf offset is initiated by unfavourable environmental conditions that incur carbon losses and these include shorter day length, cooler temperatures, and dry soil moisture conditions. The comparison of simulated leaf onset and offset times with observation-based estimates for temperate and boreal deciduous, tropical evergreen, and tropical deciduous plant functional types at selected locations indicates that the phenology scheme performs satisfactorily. Model simulated leaf area index and stem and root biomass are also compared with observational estimates to illustrate the performance of CTEM. [ABSTRACT FROM AUTHOR]

Available energy (often expressed in terms of potential evaporation) and precipitation largely determine annual evapotranspiration and runoff rates in a region. The ratio of annual potential evaporation to precipitation, referred to as the aridity index by Budyko, has been shown to describe the evaporation ratio (the ratio of annual evapotranspiration to precipitation) of catchments from a range of climatic regimes in a number of studies. It has been shown that aridity index alone can be used to obtain an estimate of ratio of standard deviation of annual evapotranspiration estimates to that of precipitation (the evaporation deviation ratio). At present, there are at least five functional forms available, which describe evaporation ratio as a function of aridity index. This study assesses data from Canadian Centre for Climate Modelling and Analysis'' (CCCma) third-generation atmospheric general circulation model (AGCM) against these five functional forms. Evaporation ratio and evaporation deviation ratios from an AGCM simulation are compared against these five functional forms and it is shown that the primary control of available energy and precipitation over annual partitioning of precipitation, and interannual variability of evapotranspiration, is preserved well in the AGCM. The aridity index is further used to obtain an analytic equation, which can be used to estimate change in runoff given annual changes in precipitation and available energy. This equation is validated using data from control and climate change simulations of the CCCma coupled GCM (CGCM1) and shown to perform fairly well. The correlation between CGCM1 simulated annual change in runoff and the values obtained using aridity index is consistently around 0.95, and the average bias varies between 40.5 and 50.3 mm/year, for the five functional forms. The successful validation of this equation against data from a GCM climate change simulation illustrates the continued relevance of aridity index, and the primary control of precipitation and available energy in determining annual evapotranspiration and runoff rates. [Copyright &y& Elsevier]