Search

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 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.

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

Soil temperature (Ts/change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of Ts determines the activelayer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing Ts not only drives permafrost thaw/retreat but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960-2000, to characterize the warming rate of Ts in permafrost regions. There is a large spread of Ts trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 °C yr-1. Most models show smaller increase in Ts with increasing depth. Air temperature (Ta/and longwave downward radiation (LWDR) are the main drivers of Ts trends, but their relative contributions differ amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in Ts trends, while trends of Ta only explain 5 % of the differences in Ts trends. Uncertain climate forcing contributes a larger uncertainty in Ts trends (0.021 ± 0.008 °C yr-1, mean ± standard deviation) than the uncertainty of model structure (0.012 ± 0.001 °C yr-1/, diagnosed from the range of response between different models, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active-layer thickness (ALT) is less than 3 m loss rate, is found to be significantly correlated with the magnitude of the trends of Ts at 1 m depth across the models (R D-0:85, P = 0:003), but not with the initial total nearsurface permafrost area (R =-0:30, P = 0:438). The sensitivity of the total boreal near-surface permafrost area to Ts at 1 m is estimated to be of-2.80 ± 0.67 million km2 °C-1. Finally, by using two long-term LWDR data sets and relationships between trends of LWDR and Ts across models, we infer an observation-constrained total boreal near-surface permafrost area decrease comprising between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr-1 from 1960 to 2000. This corresponds to 9-18 % degradation of the current permafrost area.

Soil is currently thought to be a sink for carbon; however, the response of this sink to increasing levels of atmospheric carbon dioxide and climate change is uncertain. In this study, we analyzed soil organic carbon (SOC) changes from 11 Earth system models (ESMs) under the historical and high radiative forcing (RCP 8.5) scenarios between 1850 and 2100. We used a reduced complexity model based on temperature and moisture sensitivities to analyze the drivers of SOC losses. ESM estimates of SOC change over the 21st century (2090–2099 minus 1997–2006) ranged from a loss of 72 Pg C to a gain 253 Pg C with a multi-model mean gain of 63 Pg C. All ESMs showed cumulative increases in both NPP (15% to 59%) and decreases in SOC turnover times (15% to 28%) over the 21st century. Most of the model-to-model variation in SOC change was explained by initial SOC stocks combined with the relative changes in soil inputs and decomposition rates (R2 = 0.88,p

A warming climate is altering land-atmosphere exchanges of carbon, with a potential for increased vegetation productivity as well as the mobilization of permafrost soil carbon stores. Here we investigate land-atmosphere carbon dioxide (CO2) cycling through analysis of net ecosystem productivity (NEP) and its component fluxes of gross primary productivity (GPP) and ecosystem respiration (ER) and soil carbon residence time, simulated by a set of land surface models (LSMs) over a region spanning the drainage basin of Northern Eurasia. The retrospective simulations cover the period 1960-2009 at 0.5° resolution, which is a scale common among many global carbon and climate model simulations. Model performance benchmarks were drawn from comparisons against both observed CO2 fluxes derived from site-based eddy covariance measurements as well as regional-scale GPP estimates based on satellite remote-sensing data. The site-based comparisons depict a tendency for overestimates in GPP and ER for several of the models, particularly at the two sites to the south. For several models the spatial pattern in GPP explains less than half the variance in the MODIS MOD17 GPP product. Across the models NEP in- creases by as little as 0.01 to as much as 0.79 g Cm-2 yr-2, equivalent to 3 to 340% of the respective model means, over the analysis period. For the multimodel average the increase is 135% of the mean from the first to last 10 years of record (1960-1969 vs. 2000-2009), with a weakening CO2 sink over the latter decades. Vegetation net primary productivity increased by 8 to 30% from the first to last 10 years, contributing to soil carbon storage gains. The range in regional mean NEP among the group is twice the multimodel mean, indicative of the uncertainty in CO2 sink strength. The models simulate that inputs to the soil carbon pool exceeded losses, resulting in a net soil carbon gain amid a decrease in residence time. Our analysis points to improvements in model elements controlling vegetation productivity and soil respiration as being needed for reducing uncertainty in land-atmosphere CO2 exchange. These advances will require collection of new field data on vegetation and soil dynamics, the development of benchmarking data sets from measurements and remote-sensing observations, and investments in future model development and intercomparison studies. [ABSTRACT FROM AUTHOR]

Here, the authors describe the construction of a forcing dataset for Land Surface Models (including both physical and biogeochemical models; LSMs) with eight meteorological variables for the 35 year period from 1979 to 2013. The dataset is intended for use in a model intercomparison (MIP) study, called GTMIP, which is a part of the Japanese-funded Arctic Climate Change research project. In order to prepare a set of site-fitted forcing data for LSMs with realistic yet continuous entries (i.e. without missing data), four observational sites across the pan-Arctic region (Fairbanks, Tiksi, Yakutsk, and Kevo) were selected to construct a blended dataset using both global reanalysis and observational data. Marked improvements were found in the diurnal cycles of surface air temperature and humidity, wind speed, and precipitation. The datasets and participation in GTMIP are open to the scientific community [ABSTRACT FROM AUTHOR]

As part of the terrestrial branch of the Japan-funded Arctic Climate Change Research Project (GRENE-TEA), which aims to clarify the role and function of the Arctic terrestrial system in the climate system, and assess the influence of its changes on a global 5 scale, this model intercomparison project (GTMIP) is planned and being conducted to (1) enhance communication and understanding between the "minds and hands" (i.e., between the modelling and field scientists) and (2) assess the uncertainty and variations stemming from variability in model implementation/design and in model outputs due to climatic and historical conditions in the Arctic terrestrial regions. This paper 10 provides an overview and the experiment protocol of Stage 1 of the project, site simulations driven by statistically fitted data created using the GRENE-TEA site observations for the last three decades. The target metrics for the model evaluation cover key processes in both physics and biogeochemistry, including energy budgets, snow, permafrost, phenology, and carbon budgets. The preliminary results on four metrics 15 (annual mean latent heat flux, annual maximum snow depth, gross primary production, and net ecosystem production) already demonstrate the range of variations in reproducibility among existing models and sites. Full analysis on annual as well as seasonal time scales, to be conducted upon completion of model outputs submission, will delineate inter-dependence among the key processes, and provide the clue for improving 20 the model performance. [ABSTRACT FROM AUTHOR]

The article presents a study that investigates the effectiveness of Earth System Model (ESM) in determining carbon-cycle climate feedback and the future climate. It describes the method of the study that analyzes the paleoclimate experiments proposed by the Coupled Model Intercomparison Project. It notes the result of the study, which shows that the complexity of the model requires various steps to correctly configure the experiments.

The importance of evaluating models using paleoclimate simulations is becoming more recognized in efforts to improve climate projection. To evaluate an integrated Earth System Model, MIROC-ESM, we performed simulations in time-slice experiments for the mid-Holocene (6000 yr before present, 6 ka) and preindustrial (1850 AD) times under the protocol of the Coupled Model Intercomparison Project 5/Paleoclimate Modeling Intercomparison Project 3. We first overview the simulated global climates by comparing with simulations using a previous version of the MIROC model (MIROC3), which is an atmosphere-ocean coupled general circulation model, and then comprehensively discuss various aspects of climate change with 6 ka forcing. We also discuss the 6 ka African monsoon activity. The 6 ka precipitation change over northern Africa according to MIROC-ESM does not differ dramatically from that obtained with MIROC3, which means that newly developed components such as dynamic vegetation and improvements in the atmospheric processes do not have significant impacts on representing the 6 ka monsoon change suggested by proxy records. Although there is no drastic difference in the African monsoon representation between the two models, there are small but significant differences in the precipitation enhancement in MIROC-ESM, which can be related to the representation of the sea surface temperature rather than the vegetation coupling, at least in MIROC-ESM. [ABSTRACT FROM AUTHOR]

The article describes the earth system model MIROC-ESM in relation to each model component and their interactions. It presents the findings for the Coupled Model Intercomparison Project phase 5 (CMIP5) to illustrate the performance of the model from different perspectives including atmospheric chemistry, terrestrial biogeochemistry and aerosols. Also discussed is the reasonable simulation of the global distribution of column ozone in the model.

Terrestrial biosphere models show large uncertainties when simulating carbon and water cycles, and reducing these uncertainties is a priority for developing more accurate estimates of both terrestrial ecosystem statuses and future climate changes. To reduce uncertainties and improve the understanding of these carbon budgets, we investigated the ability of flux datasets to improve model simulations and reduce variabilities among multi-model outputs of terrestrial biosphere models in Japan. Using 9 terrestrial biosphere models (Support Vector Machine-based regressions, TOPS, CASA, VISIT, Biome-BGC, DAYCENT, SEIB, LPJ, and TRIFFID), we conducted two simulations: (1) point simulations at four flux sites in Japan and (2) spatial simulations for Japan with a default model (based on original settings) and an improved model (based on calibration using flux observations). Generally, models using default model settings showed large deviations in model outputs from observation with large model-by-model variability. However, after we calibrated the model parameters using flux observations (GPP, RE and NEP), most models successfully simulated seasonal variations in the carbon cycle, with less variability among models. We also found that interannual variations in the carbon cycle are mostly consistent among models and observations. Spatial analysis also showed a large reduction in the variability among model outputs, and model calibration using flux observations significantly improved the model outputs. These results show that to reduce uncertainties among terrestrial biosphere models, we need to conduct careful validation and calibration with available flux observations. Flux observation data significantly improved terrestrial biosphere models, not only on a point scale but also on spatial scales. [ABSTRACT FROM AUTHOR]

Nutrient inputs from the atmosphere and rivers to the ocean are increased substantially by human activities. However, the effects of increased nutrient inputs are not included in the widely used CMIP5 Earth system models, which introduce bias into model simulations of ocean biogeochemistry. Here, using historical simulations by an Earth system model with perturbed atmospheric and riverine nutrient inputs, we show that the contribution of anthropogenic nutrient inputs to past global changes in ocean biogeochemistry is of similar magnitude to the effect of climate change. Anthropogenic nutrient inputs increase oceanic productivity and carbon uptake, offsetting climate-induced decrease and accelerating climate-driven deoxygenation in the upper ocean. Moreover, accounting for anthropogenic nutrient inputs improves the known carbon budget imbalance and model underestimation of the observed decrease in the global oxygen inventory. Considering the effects of both nutrient inputs and climate change is crucial in assessing anthropogenic impacts on ocean biogeochemistry.

Both low soil water content (SWC) and high atmospheric dryness (vapor pressure deficit, VPD) can negatively affect terrestrial gross primary production (GPP). The sensitivity of GPP to soil versus atmospheric dryness is difficult to disentangle, however, because of their covariation. Using global eddy-covariance observations, here we show that a decrease in SWC is not universally associated with GPP reduction. GPP increases in response to decreasing SWC when SWC is high and decreases only when SWC is below a threshold. By contrast, the sensitivity of GPP to an increase of VPD is always negative across the full SWC range. We further find canopy conductance decreases with increasing VPD (irrespective of SWC), and with decreasing SWC on drier soils. Maximum photosynthetic assimilation rate has negative sensitivity to VPD, and a positive sensitivity to decreasing SWC when SWC is high. Earth System Models underestimate the negative effect of VPD and the positive effect of SWC on GPP such that they should underestimate the GPP reduction due to increasing VPD in future climates., (© 2022. The Author(s).)

To date, the treatment of permafrost in global climate models has been simplified due to the prevailing uncertainties in the processes involving frozen ground. In this study, we improved the modeling of permafrost processes in a state-of-the-art climate model by taking into account some of the relevant physical properties of soil such as changes in the thermophysical properties due to soil freezing. As a result, the improved version of the global land surface model was able to reproduce a more realistic permafrost distribution at the southern limit of the permafrost area by increasing the freezing of soil moisture in winter. The improved modeling of permafrost processes also had a significant effect on future projections. Using the conventional formulation, the predicted cumulative reduction of the permafrost area by year 2100 was approximately 60% (40-80% range of uncertainty from a multi-model ensemble) in the RCP8.5 scenario, while with the improved formulation, the reduction was approximately 35% (20-50%). Our results indicate that the improved treatment of permafrost processes in global climate models is important to ensuring more reliable future projections., Competing Interests: Competing interestsThe authors declare that they have no competing interests, (© The Author(s) 2020.)

The Yedoma layer, a permafrost layer containing a massive amount of underground ice in the Arctic regions, is reported to be rapidly thawing. In this study, we develop the Permafrost Degradation and Greenhouse gasses Emission Model (PDGEM), which describes the thawing of the Arctic permafrost including the Yedoma layer due to climate change and the greenhouse gas (GHG) emissions. The PDGEM includes the processes by which high-concentration GHGs (CO 2 and CH 4 ) contained in the pores of the Yedoma layer are released directly by dynamic degradation, as well as the processes by which GHGs are released by the decomposition of organic matter in the Yedoma layer and other permafrost. Our model simulations show that the total GHG emissions from permafrost degradation in the RCP8.5 scenario was estimated to be 31-63 PgC for CO 2 and 1261-2821 TgCH 4 for CH 4 (68 th percentile of the perturbed model simulations, corresponding to a global average surface air temperature change of 0.05-0.11 °C), and 14-28 PgC for CO 2 and 618-1341 TgCH 4 for CH 4 (0.03-0.07 °C) in the RCP2.6 scenario. GHG emissions resulting from the dynamic degradation of the Yedoma layer were estimated to be less than 1% of the total emissions from the permafrost in both scenarios, possibly because of the small area ratio of the Yedoma layer. An advantage of PDGEM is that geographical distributions of GHG emissions can be estimated by combining a state-of-the-art land surface model featuring detailed physical processes with a GHG release model using a simple scheme, enabling us to consider a broad range of uncertainty regarding model parameters. In regions with large GHG emissions due to permafrost thawing, it may be possible to help reduce GHG emissions by taking measures such as restraining land development., Competing Interests: Competing interestsThe authors declare that they have no competing interest., (© The Author(s) 2020.)

The regional budget of methane (CH 4 ) emissions for East Asia, a crucial region in the global greenhouse gas budget, was quantified for 1990-2015 with a bottom-up method based on inventories and emission model simulations. Anthropogenic emissions associated with fossil fuel extraction, industrial activities, waste management, and agricultural activities were derived from the Emission Database for Global Atmospheric Research version 4.3.2 and compared with other inventories. Emissions from natural wetlands and CH 4 uptake by upland soil oxidation were estimated using the Vegetation Integrative SImulator for Trace gases (VISIT), a biogeochemical model that considers historical land use and climatic conditions. Emissions from biomass burning and termites were calculated using satellite and land-use data combined with empirical emission factors. The resulting average annual estimated CH 4 budget for 2000-2012 indicated that East Asia was a net source of 67.3 Tg CH 4 yr -1 , of which 88.8% was associated with anthropogenic emissions. The uncertainty (±standard deviation) of this estimate, ±14 Tg CH 4 yr -1 , stemmed from data and model inconsistencies. The increase of the net flux from 60.2 Tg CH 4 yr -1 in 1990 to 78.0 Tg CH 4 yr -1 in 2012 was due mainly to increased emissions by the fossil fuel extraction and livestock sectors. Our results showed that CH 4 was a crucial component of the regional greenhouse gas budget. A spatial analysis using 0.25° × 0.25° grid cells revealed emission hotspots in urban areas, agricultural areas, and wetlands. These hotspots were surrounded by weak sinks in upland areas. The estimated natural and anthropogenic emissions fell within the range of independent estimates, including top-down estimates from atmospheric inversion models. Such a regional accounting is an effective way to elucidate climatic forcings and to develop mitigation policies. Further studies, however, are required to reduce the uncertainties in the budget., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Nitrification in terrestrial soils is one of the major processes of emission of nitrous oxide (N2O), a potent greenhouse gas and stratospheric-ozone-depleting substance. We assessed the fraction of N2O emission associated with nitrification in soil through a meta-analysis and sensitivity analysis using a process-based model. We corrected observational values of gross nitrification and associated N2O emission rates from 71 records for various soils in the world spanning from 0.006% to 29.5%. We obtained a median value of 0.14%, and then assessed how the nitrification-associated N2O emission fraction has been considered in terrestrial nitrogen cycle models. Using a process-based biogeochemical model, we conducted a series of sensitivity analyses for the effects of different values of nitrification-associated N2O emission fraction on soil N2O emission. Using an empirical relationship between soil pH and nitrification-associated N2O emission fraction, the model well simulated global emission patterns (global total in the 2000s, 16.8 Tg N2O yr-1). Differences in the nitrification-associated N2O emission fraction caused differences in total N2O emission of as much as 2.5 Tg N2O yr-1. Therefore, to obtain reliable estimation of soil N2O emission for nitrogen and climate management, it is important to constrain the parameterization in models by ensuring extensive and accurate observations., Competing Interests: The authors have declared that no competing interests exist.

Minute virus of canines (MVC), also known as canine parvovirus type 1, was initially believed to be a nonpathogenic agent, since it was first isolated from canine fecal specimens in the late 1960s. However, subsequent pathological as well as epidemiological studies suggested that MVC is a pathogen of neonatal puppies and is widely distributed among domestic dogs in the United States. The virus also has been shown to cause fetal deaths. Nevertheless, the virus was not detected in dogs outside the United States until recently, presumably because of a lack of widespread availability of the only susceptible canine cell line, WRCC/3873D, used for MVC isolation. We examined 470 clinical specimens from 346 dogs by PCR and detected MVC-specific gene fragments from four diseased puppies (positive rate, 1.2%). Viruses were recovered from three PCR-positive rectal specimens by using WRCC/3873D and MDCK cells. The isolates possessed antigenic and genomic properties similar to those of the U.S. reference strain GA3 and were identified as MVC. In addition, seroepidemiological evidence that 5.0% of dogs possessed anti-MVC antibodies also indicated the presence of MVC infection among dogs in Japan. From this study and several recent European reports describing MVC field cases, it is evident that MVC is distributed among domestic dogs worldwide.