We analyse how climate change may alter risks posed by droughts to carbon fluxes in European ecosystems. The approach follows a recently proposed framework for risk analysis based on probability ...theory. In this approach, risk is quantified as the product of hazard probability and ecosystem vulnerability. The probability of a drought hazard is calculated here from the Standardized Precipitation–Evapotranspiration Index (SPEI). Vulnerability is calculated from the response to drought simulated by process-based vegetation models. We use six different models: three for generic vegetation (JSBACH, LPJmL, ORCHIDEE) and three for specific ecosystems (Scots pine forests: BASFOR; winter wheat fields: EPIC; grasslands: PASIM). The periods 1971–2000 and 2071–2100 are compared. Climate data are based on gridded observations and on output from the regional climate model REMO using the SRES A1B scenario. The risk analysis is carried out for ~ 18 000 grid cells of 0.25 × 0.25° across Europe. For each grid cell, drought vulnerability and risk are quantified for five seasonal variables: net primary and ecosystem productivity (NPP, NEP), heterotrophic respiration (Rh), soil water content and evapotranspiration. In this analysis, climate change leads to increased drought risks for net primary productivity in the Mediterranean area: five of the models estimate that risk will exceed 15%. The risks increase mainly because of greater drought probability; ecosystem vulnerability will increase to a lesser extent. Because NPP will be affected more than Rh, future carbon sequestration (NEP) will also be at risk predominantly in southern Europe, with risks exceeding 0.25 g C m−2 d−1 according to most models, amounting to reductions in carbon sequestration of 20 to 80%.
A detailed process-based methane module for a global land surface scheme has been developed which is general enough to be applied in permafrost regions as well as wetlands outside permafrost areas. ...Methane production, oxidation and transport by ebullition, diffusion and plants are represented. In this model, oxygen has been explicitly incorporated into diffusion, transport by plants and two oxidation processes, of which one uses soil oxygen, while the other uses oxygen that is available via roots. Permafrost and wetland soils show special behaviour, such as variable soil pore space due to freezing and thawing or water table depths due to changing soil water content. This has been integrated directly into the methane-related processes. A detailed application at the Samoylov polygonal tundra site, Lena River Delta, Russia, is used for evaluation purposes. The application at Samoylov also shows differences in the importance of the several transport processes and in the methane dynamics under varying soil moisture, ice and temperature conditions during different seasons and on different microsites. These microsites are the elevated moist polygonal rim and the depressed wet polygonal centre. The evaluation shows sufficiently good agreement with field observations despite the fact that the module has not been specifically calibrated to these data. This methane module is designed such that the advanced land surface scheme is able to model recent and future methane fluxes from periglacial landscapes across scales. In addition, the methane contribution to carbon cycle–climate feedback mechanisms can be quantified when running coupled to an atmospheric model.
Temporal variability of meteorological variables and extreme weather events is projected to increase in many regions of the world during the next century. Artificial experiments using ...process-oriented terrestrial ecosystem models make it possible to isolate effects of temporal variability from effects of gradual climate change on terrestrial ecosystem functions and the system state. Such factorial experiments require two long-term climate datasets: 1) a control dataset that represents observed and projected climate and 2) a dataset with the same long-term mean as the control dataset but with altered short-term variability. Using a bias correction method, various climate datasets spanning different periods are harmonized and then combined with the control dataset with consistent time series for Europe during 1901–2100. Then, parameters of a distribution transformation function are estimated for individual meteorological variables to derive the second climate dataset, which has similar long-term means but reduced temporal variability. The transformation conserves the number of rainy days within a month and the shape of the daily meteorological data distributions, which is important to ensure that, for example, drought duration does not modify the suitability of localized vegetation type to precipitation regimes. The median absolute difference between daily data of both datasets is 5% to 20%. On average, decadal extreme values are reduced by 2% to 35%. Driving a terrestrial ecosystem model with both climate datasets shows a general higher gross primary production under reduced temporal climate variability. This effect of climate variability on productivity demonstrates the potential of the climate datasets for studying various effects of temporal variability on ecosystem state and functions over large domains.
Temporal variability of meteorological variables and extreme weather events is projected to increase in many regions of the world during the next century. Artificial experiments using ...process-oriented terrestrial ecosystem models make it possible to isolate effects of temporal variability from effects of gradual climate change on terrestrial ecosystem functions and the system state. Such factorial experiments require two long-term climate datasets: 1) a control dataset that represents observed and projected climate and 2) a dataset with the same long-term mean as the control dataset but with altered short-term variability. Using a bias correction method, various climate datasets spanning different periods are harmonized and then combined with the control dataset with consistent time series for Europe during 1901-2100. Then, parameters of a distribution transformation function are estimated for individual meteorological variables to derive the second climate dataset, which has similar long-term means but reduced temporal variability. The transformation conserves the number of rainy days within a month and the shape of the daily meteorological data distributions, which is important to ensure that, for example, drought duration does not modify the suitability of localized vegetation type to precipitation regimes. The median absolute difference between daily data of both datasets is 5% to 20%. On average, decadal extreme values are reduced by 2% to 35%. Driving a terrestrial ecosystem model with both climate datasets shows a general higher gross primary production under reduced temporal climate variability. This effect of climate variability on productivity demonstrates the potential of the climate datasets for studying various effects of temporal variability on ecosystem state and functions over large domains.
Abstract
The currently observed
A
rctic warming will increase permafrost degradation followed by mineralization of formerly frozen organic matter to carbon dioxide (
CO
2
) and methane (
CH
4
). ...Despite increasing awareness of permafrost carbon vulnerability, the potential long‐term formation of trace gases from thawing permafrost remains unclear. The objective of the current study is to quantify the potential long‐term release of trace gases from permafrost organic matter. Therefore,
H
olocene and
P
leistocene permafrost deposits were sampled in the
L
ena
R
iver
D
elta,
N
ortheast
S
iberia. The sampled permafrost contained between 0.6% and 12.4% organic carbon.
CO
2
and
CH
4
production was measured for 1200 days in aerobic and anaerobic incubations at 4 °C. The derived fluxes were used to estimate parameters of a two pool carbon degradation model. Total
CO
2
production was similar in Holocene permafrost (1.3 ± 0.8 mg
CO
2
‐C gdw
−1
aerobically, 0.25 ± 0.13 mg
CO
2
‐C gdw
−1
anaerobically) as in 34 000–42 000‐year‐old
P
leistocene permafrost (1.6 ± 1.2 mg
CO
2
‐C gdw
−1
aerobically, 0.26 ± 0.10 mg
CO
2
‐C gdw
−1
anaerobically). The main predictor for carbon mineralization was the content of organic matter. Anaerobic conditions strongly reduced carbon mineralization since only 25% of aerobically mineralized carbon was released as
CO
2
and
CH
4
in the absence of oxygen.
CH
4
production was low or absent in most of the
P
leistocene permafrost and always started after a significant delay. After 1200 days on average 3.1% of initial carbon was mineralized to
CO
2
under aerobic conditions while without oxygen 0.55% were released as
CO
2
and 0.28% as
CH
4
. The calibrated carbon degradation model predicted cumulative
CO
2
production over a period of 100 years accounting for 15.1% (aerobic) and 1.8% (anaerobic) of initial organic carbon, which is significantly less than recent estimates. The multiyear time series from the incubation experiments helps to more reliably constrain projections of future trace gas fluxes from thawing permafrost landscapes.
It is important that climate models can accurately simulate the terrestrial carbon cycle in the Arctic due to the large and potentially labile carbon stocks found in permafrost-affected environments, ...which can lead to a positive climate feedback, along with the possibility of future carbon sinks from northward expansion of vegetation under climate warming. Here we evaluate the simulation of tundra carbon stocks and fluxes in three land surface schemes that each form part of major Earth system models (JSBACH, Germany; JULES, UK; ORCHIDEE, France). We use a site-level approach in which comprehensive, high-frequency datasets allow us to disentangle the importance of different processes. The models have improved physical permafrost processes and there is a reasonable correspondence between the simulated and measured physical variables, including soil temperature, soil moisture and snow.
We show that if the models simulate the correct leaf area index (LAI), the standard C3 photosynthesis schemes produce the correct order of magnitude of carbon fluxes. Therefore, simulating the correct LAI is one of the first priorities. LAI depends quite strongly on climatic variables alone, as we see by the fact that the dynamic vegetation model can simulate most of the differences in LAI between sites, based almost entirely on climate inputs. However, we also identify an influence from nutrient limitation as the LAI becomes too large at some of the more nutrient-limited sites. We conclude that including moss as well as vascular plants is of primary importance to the carbon budget, as moss contributes a large fraction to the seasonal CO2 flux in nutrient-limited conditions. Moss photosynthetic activity can be strongly influenced by the moisture content of moss, and the carbon uptake can be significantly different from vascular plants with a similar LAI.
The soil carbon stocks depend strongly on the rate of input of carbon from the vegetation to the soil, and our analysis suggests that an improved simulation of photosynthesis would also lead to an improved simulation of soil carbon stocks. However, the stocks are also influenced by soil carbon burial (e.g. through cryoturbation) and the rate of heterotrophic respiration, which depends on the soil physical state. More detailed below-ground measurements are needed to fully evaluate biological and physical soil processes. Furthermore, even if these processes are well modelled, the soil carbon profiles cannot resemble peat layers as peat accumulation processes are not represented in the models.
Thus, we identify three priority areas for model development: (1) dynamic vegetation including (a) climate and (b) nutrient limitation effects; (2) adding moss as a plant functional type; and an (3) improved vertical profile of soil carbon including peat processes.
Changes to climate–carbon cycle feedbacks may significantly affect the Earth system's response to greenhouse gas emissions. These feedbacks are usually analysed from numerical output of complex and ...arguably opaque Earth system models. Here, we construct a stylised global climate–carbon cycle model, test its output against comprehensive Earth system models, and investigate the strengths of its climate–carbon cycle feedbacks analytically. The analytical expressions we obtain aid understanding of carbon cycle feedbacks and the operation of the carbon cycle. Specific results include that different feedback formalisms measure fundamentally the same climate–carbon cycle processes; temperature dependence of the solubility pump, biological pump, and CO2 solubility all contribute approximately equally to the ocean climate–carbon feedback; and concentration–carbon feedbacks may be more sensitive to future climate change than climate–carbon feedbacks. Simple models such as that developed here also provide workbenches for simple but mechanistically based explorations of Earth system processes, such as interactions and feedbacks between the planetary boundaries, that are currently too uncertain to be included in comprehensive Earth system models.
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