ORCID Profile
0000-0001-9477-563X
Current Organisation
Lund University
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Publisher: American Geophysical Union (AGU)
Date: 16-05-2022
DOI: 10.1029/2021GL095276
Abstract: Peatlands store large amounts of carbon in terrestrial ecosystems and they are vulnerable to recent warming. The ongoing warming may change their carbon sink capacity and could reduce their potential to sequester carbon. In this study, we simulated peatland carbon dynamics in distinct future climate conditions using the peatland‐vegetation model (LPJ‐GUESS). The study examined whether less pronounced warming could further enhance the peatland carbon sink capacity and buffer the effects of climate change. It also determined which trajectory peatland carbon balance would follow, what the main drivers were, and which one would dominate in the future. We found that peatlands will largely retain their carbon sink capacity under the climate scenario RCP2.6 to RCP6.0. They are projected to shift from a carbon sink to a carbon‐neutral (5–10 gC m −2 yr −1 ) in RCP8.5. Higher respiration rates will dominate the net productivity in a warmer world leading to a reduction in carbon sink capacity.
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 04-2023
Publisher: Copernicus GmbH
Date: 11-03-2016
Publisher: Copernicus GmbH
Date: 28-07-2015
Abstract: Abstract. 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 increases by as little as 0.01 to as much as 0.79 g C m−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.
Publisher: IOP Publishing
Date: 29-08-2013
Publisher: Copernicus GmbH
Date: 11-03-2016
DOI: 10.5194/TC-2016-36
Abstract: Abstract. A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyze 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 across-model differences as expressed by simulated differences between near-surface soil and air temperatures, (ΔT), of 3 to 14 K, in the gradients between soil and air temperatures (0.13 to 0.96 °C/°C), 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, and hence guide improvements to the model’s conceptual structure and process parameterizations. Models with better performance apply multi-layer 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 (12–16 million km2). However, there is not a simple relationship between the quality of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, likely because several other factors such as differences in the treatment of soil organic matter, soil hydrology, surface energy calculations, and vegetation also provide important controls on simulated permafrost distribution.
Publisher: American Geophysical Union (AGU)
Date: 16-03-2020
DOI: 10.1029/2019GL085982
Publisher: Copernicus GmbH
Date: 15-07-2020
DOI: 10.5194/ESSD-12-1561-2020
Abstract: Abstract. Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Atmospheric emissions and concentrations of CH4 continue to increase, making CH4 the second most important human-influenced greenhouse gas in terms of climate forcing, after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 depends on its shorter atmospheric lifetime, stronger warming potential, and variations in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the atmospheric growth rate arise from the variety of geographically overlapping CH4 sources and from the destruction of CH4 by short-lived hydroxyl radicals (OH). To address these challenges, we have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. Following Saunois et al. (2016), we present here the second version of the living review paper dedicated to the decadal methane budget, integrating results of top-down studies (atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up estimates (including process-based models for estimating land surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). For the 2008–2017 decade, global methane emissions are estimated by atmospheric inversions (a top-down approach) to be 576 Tg CH4 yr−1 (range 550–594, corresponding to the minimum and maximum estimates of the model ensemble). Of this total, 359 Tg CH4 yr−1 or ∼ 60 % is attributed to anthropogenic sources, that is emissions caused by direct human activity (i.e. anthropogenic emissions range 336–376 Tg CH4 yr−1 or 50 %–65 %). The mean annual total emission for the new decade (2008–2017) is 29 Tg CH4 yr−1 larger than our estimate for the previous decade (2000–2009), and 24 Tg CH4 yr−1 larger than the one reported in the previous budget for 2003–2012 (Saunois et al., 2016). Since 2012, global CH4 emissions have been tracking the warmest scenarios assessed by the Intergovernmental Panel on Climate Change. Bottom-up methods suggest almost 30 % larger global emissions (737 Tg CH4 yr−1, range 594–881) than top-down inversion methods. Indeed, bottom-up estimates for natural sources such as natural wetlands, other inland water systems, and geological sources are higher than top-down estimates. The atmospheric constraints on the top-down budget suggest that at least some of these bottom-up emissions are overestimated. The latitudinal distribution of atmospheric observation-based emissions indicates a predominance of tropical emissions (∼ 65 % of the global budget, 30∘ N) compared to mid-latitudes (∼ 30 %, 30–60∘ N) and high northern latitudes (∼ 4 %, 60–90∘ N). The most important source of uncertainty in the methane budget is attributable to natural emissions, especially those from wetlands and other inland waters. Some of our global source estimates are smaller than those in previously published budgets (Saunois et al., 2016 Kirschke et al., 2013). In particular wetland emissions are about 35 Tg CH4 yr−1 lower due to improved partition wetlands and other inland waters. Emissions from geological sources and wild animals are also found to be smaller by 7 Tg CH4 yr−1 by 8 Tg CH4 yr−1, respectively. However, the overall discrepancy between bottom-up and top-down estimates has been reduced by only 5 % compared to Saunois et al. (2016), due to a higher estimate of emissions from inland waters, highlighting the need for more detailed research on emissions factors. Priorities for improving the methane budget include (i) a global, high-resolution map of water-saturated soils and inundated areas emitting methane based on a robust classification of different types of emitting habitats (ii) further development of process-based models for inland-water emissions (iii) intensification of methane observations at local scales (e.g., FLUXNET-CH4 measurements) and urban-scale monitoring to constrain bottom-up land surface models, and at regional scales (surface networks and satellites) to constrain atmospheric inversions (iv) improvements of transport models and the representation of photochemical sinks in top-down inversions and (v) development of a 3D variational inversion system using isotopic and/or co-emitted species such as ethane to improve source partitioning. The data presented here can be downloaded from 0.18160/GCP-CH4-2019 (Saunois et al., 2020) and from the Global Carbon Project.
Publisher: IOP Publishing
Date: 06-2021
Abstract: Arctic cyclones, as a prevalent feature in the coupled dynamics of the Arctic climate system, have large impacts on the atmospheric transport of heat and moisture and deformation and drifting of sea ice. Previous studies based on historical and future simulations with climate models suggest that Arctic cyclogenesis is affected by the Arctic lification of global warming, for instance, a growing land-sea thermal contrast. We thus hypothesize that biogeophysical feedbacks (BF) over the land, here mainly referring to the albedo-induced warming in spring and evaporative cooling in summer, may have the potential to significantly change cyclone activity in the Arctic. Based on a regional Earth system model (RCA-GUESS) which couples a dynamic vegetation model and a regional atmospheric model and an algorithm of cyclone detection and tracking, this study assesses for the first time the impacts of BF on the characteristics of Arctic cyclones under three IPCC Representative Concentration Pathways scenarios (i.e. RCP2.6, RCP4.5 and RCP8.5). Our analysis focuses on the spring- and summer time periods, since previous studies showed BF are the most pronounced in these seasons. We find that BF induced by changes in surface heat fluxes lead to changes in land-sea thermal contrast and atmospheric stability. This, in turn, noticeably changes the atmospheric baroclinicity and, thus, leads to a change of cyclone activity in the Arctic, in particular to the increase of cyclone frequency over the Arctic Ocean in spring. This study highlights the importance of accounting for BF in the prediction of Arctic cyclones and the role of circulation in the Arctic regional Earth system.
Publisher: American Geophysical Union (AGU)
Date: 07-2016
DOI: 10.1002/2016GB005405
Publisher: Wiley
Date: 15-05-2023
DOI: 10.1111/GCB.16755
Abstract: The recent rise in atmospheric methane (CH 4 ) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH 4 source, estimates of global wetland CH 4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH 4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH 4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH 4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH 4 year −1 ) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to erse environmental conditions. We encourage future development of wetland CH 4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.
Publisher: American Geophysical Union (AGU)
Date: 02-2017
DOI: 10.1002/2016JG003384
Publisher: Copernicus GmbH
Date: 12-05-2014
Abstract: Abstract. Continued warming of the Arctic will likely accelerate terrestrial carbon (C) cycling by increasing both uptake and release of C. There are still large uncertainties in modelling Arctic terrestrial ecosystems as a source or sink of C. Most modelling studies assessing or projecting the future fate of C exchange with the atmosphere are based an either stand-alone process-based models or coupled climate–C cycle general circulation models, in either case disregarding biogeophysical feedbacks of land surface changes to the atmosphere. To understand how biogeophysical feedbacks will impact on both climate and C budget over Arctic terrestrial ecosystems, we apply the regional Earth system model RCA-GUESS over the CORDEX-Arctic domain. The model is forced with lateral boundary conditions from an GCMs CMIP5 climate projection under the RCP 8.5 scenario. We perform two simulations with or without interactive vegetation dynamics respectively to assess the impacts of biogeophysical feedbacks. Both simulations indicate that Arctic terrestrial ecosystems will continue to sequester C with an increased uptake rate until 2060s–2070s, after which the C budget will return to a weak C sink as increased soil respiration and biomass burning outpaces increased net primary productivity. The additional C sinks arising from biogeophysical feedbacks are considerable, around 8.5 Gt C, accounting for 22% of the total C sinks, of which 83.5% are located in areas of Arctic tundra. Two opposing feedback mechanisms, mediated by albedo and evapotranspiration changes respectively, contribute to this response. Albedo feedback dominates over winter and spring season, lifying the near-surface warming by up to 1.35 K in spring, while evapotranspiration feedback dominates over summer exerting the evaporative cooling by up to 0.81 K. Such feedbacks stimulate vegetation growth with an earlier onset of growing-season, leading to compositional changes in woody plants and vegetation redistribution.
Publisher: Copernicus GmbH
Date: 08-10-2014
Abstract: Abstract. Continued warming of the Arctic will likely accelerate terrestrial carbon (C) cycling by increasing both uptake and release of C. Yet, there are still large uncertainties in modelling Arctic terrestrial ecosystems as a source or sink of C. Most modelling studies assessing or projecting the future fate of C exchange with the atmosphere are based on either stand-alone process-based models or coupled climate–C cycle general circulation models, and often disregard biogeophysical feedbacks of land-surface changes to the atmosphere. To understand how biogeophysical feedbacks might impact on both climate and the C budget in Arctic terrestrial ecosystems, we apply the regional Earth system model RCA-GUESS over the CORDEX-Arctic domain. The model is forced with lateral boundary conditions from an EC-Earth CMIP5 climate projection under the representative concentration pathway (RCP) 8.5 scenario. We perform two simulations, with or without interactive vegetation dynamics respectively, to assess the impacts of biogeophysical feedbacks. Both simulations indicate that Arctic terrestrial ecosystems will continue to sequester C with an increased uptake rate until the 2060–2070s, after which the C budget will return to a weak C sink as increased soil respiration and biomass burning outpaces increased net primary productivity. The additional C sinks arising from biogeophysical feedbacks are approximately 8.5 Gt C, accounting for 22% of the total C sinks, of which 83.5% are located in areas of extant Arctic tundra. Two opposing feedback mechanisms, mediated by albedo and evapotranspiration changes respectively, contribute to this response. The albedo feedback dominates in the winter and spring seasons, lifying the near-surface warming by up to 1.35 °C in spring, while the evapotranspiration feedback dominates in the summer months, and leads to a cooling of up to 0.81 °C. Such feedbacks stimulate vegetation growth due to an earlier onset of the growing season, leading to compositional changes in woody plants and vegetation redistribution.
Publisher: Wiley
Date: 25-07-2021
Publisher: American Geophysical Union (AGU)
Date: 20-07-2018
DOI: 10.1029/2018GL077830
Publisher: Copernicus GmbH
Date: 20-01-2016
Abstract: Abstract. Soil temperature (Ts) change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of Ts determines the active-layer 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 (Tsub a) 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 = −0.85, P = 0.003), but not with the initial total near-surface 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.
Publisher: Copernicus GmbH
Date: 12-05-2014
Publisher: Copernicus GmbH
Date: 11-08-2016
Abstract: Abstract. 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.
Publisher: Elsevier BV
Date: 2022
No related grants have been discovered for Wenxin Zhang.