Significant climatic changes are currently observed and, according to projections, will be strengthened over the 21st century throughout the world with the continuing increase of the atmospheric CO2 concentration. Climate will be generally warmer with notably changes in the seasonality and in the precipitation regime. These changes will have major impacts on the biodiversity and the functioning of natural ecosystems. The CARAIB dynamic vegetation model driven by the ARPEGE/Climate model under forcing from the A2 IPCC emission scenario is used to illustrate and analyse the potential impacts of climate change on forest productivity and distribution as well as fire intensity over Europe. The potential CO2 fertilizing effect is studied throughout transient runs of the vegetation model over the 1961-2100 period assuming constant and increasing atmospheric CO2 concentration. Without fertilisation effect, the net primary productivity (NPP) might increase in high latitudes and altitudes (by up to 40 % or even 60-100 %) while it might decrease in temperate (by up to 50 %) and in warmer regions,
Climate warming raises many questions about living organisms and ecosystems. Among these, there are considerable interest in species or biome distribution prediction, carbon cycling and impacts of droughts on ecosystems and forest fires.
The problem of species distribution is mainly related to biodiversity protection. According to
The challenge of carbon cycling studies is double. On the one hand, it is important to determine how each one of the ecosystems acts, as a source or as a sink of greenhouse gases to produce a global picture of carbon cycling and refine projections (
Climate projections also indicate changes in climate variability and frequency of extreme events (
Process-based dynamic vegetation models are tools of choice to address to the study of the above problems. They can simulate the growth of various levels of primary producers, from species (
In this paper, a new version of the CARAIB (Carbon Assimilation in the Biosphere) process-based dynamic vegetation model (
The CARAIB model (
The hydrological module (
The canopy photosynthesis and stomatal regulation module. Canopy photosynthesis is based on Farquhar et al.’s (
where
where
where
The carbon allocation and plant growth module (
The heterotrophic respiration and litter/ soil carbon module (
The plant competition and biogeography module evaluates cover fraction of all Bioclimatic Affinity Groups (BAGs -
The establishment of a BAG on a grid cell depends on the availability of free space on this grid cell and on the BAG climatic requirements for germination. The relevant variables controlling germination are the yearly sum of the daily temperatures above 5 °C (GDD5), the coldest monthly mean night temperature (Tcm) and the minimum monthly soil water content (SW). For germination to be possible in a given year, the values of these variables must be either lower (Tcm and SW) or higher (GDD5) than respective thresholds (Tmaxg, SWmaxg and GDD5ming) defined for each BAG (
The initialisation of BAG cover fractions is performed assuming equal fractions for all germinated BAGs. An initial biomass of 5 g C m-2 for herb and shrub BAGs or 10 g C m-2 for tree BAGs. The cover fractions are estimated separately in each storey once a year after allocation of photosynthetic products to plant reservoirs. BAG fraction decreases are calculated daily and summed over the year. Mortality occurs owing to ageing mortality, thermal stress, water deficit stress and fire disturbances. Ageing death rate is inversely proportional to the rough estimates of BAG maximum life time,
The
The model was driven by 1961-2100 monthly mean data for mean air temperature, precipitation, diurnal temperature range, relative humidity, cloud cover (converted in percentage of sunshine hours) and surface horizontal wind speed from the ARPEGE/ Climate model (
To be able to compare CARAIB present day annual runoff, net primary productivity and fire outputs with ground data and satellite products, a simulation was also run with CRU TS 3.0 historical climate data for the 1961-2006 period at 0.5° spatial resolution (CRU TS 3.0 in preparation). Annual runoff will be compared with runoff estimated from river discharge from the UNESCO atlas (
Since ARPEGE/Climate outputs were not available for the beginning of the 20th century, initialisation was performed by running seven times the 1961-1990 ARPEGE/Climate reconstruction sequence with a 330 ppmv CO2 average concentration. Two fully transient simulations were run respectively with rising atmospheric CO2 concentration from the A2 scenario (in both the climate and vegetation models) and with constant CO2 concentration remained at 330 ppmv in the vegetation model (with climate change from A2 scenario calculated by ARPEGE/ Climate). This initialisation procedure allowed studying the model interannual variability changes between the 2081-2100 and 1981-2000 periods. The linear trend of the full 20-year data was first removed with a linear least square fit and the interannual variability was studied through temporal standard deviation (SD). Trend detection in CARAIB outputs (soil water, NPP, fire) was achieved with the JMulti 4 software (
As already shown by
Fig. 3a and 3b compare CARAIB NPP computed values with NPP estimations of MODIS sensor for grid cells with ≥ 30 % natural vegetation. A large proportion (70 %) of the CARAIB values lies in the -20 to +20 % range of the MODIS data. The largest differences occur in the Mediterranean region where CARAIB tends to underestimate MODIS NPP values (Spain, south of France, Italy) and in the northern regions where it tends to overestimate them. A second comparison of CARAIB NPP computed values is given in
As shown by
Fig. 5a shows the simulated and the observed area burned (
Forced with the A2 scenario, ARPEGE/ Climate predicts substantial warming over Europe in all seasons (in average 4.3 °C increase of the annual mean temperature). Northern Europe shows a maximum temperature increase in winter (5 to more than 9 °C) while in southern Europe and in the Mediterranean region, the warming is more pronounced in summer (5 °C by up to 8 °C in the Balkans and in Ukraine). Concerning precipitations, in winter, ARPEGE/Climate predicts decreases (0-120 mm) over most regions below 50° N, except in the Caucasus and in Central Asia and increases above this latitude (0-120 mm), except in the south of Sweden (decrease of 0-30 mm). In summer, precipitations increase above 60° N (0-80 mm) while in the other parts of Europe they are projected to decrease (0-120 mm), except in Mediterranean area and in the Alps (increase of 0-40 mm). Compared with projections of other climate models from the ENSEMBLES European project (
Fig. 6 presents the NPP relative anomalies under changing climate between the end of the 21st century (2081-2100) and the present (1981-2000) without or with CO2 fertilizing effect. The first simulation is driven with atmospheric CO2 concentration kept constant (330 ppmv) in the vegetation model but with climate change from A2 scenario calculated by ARPEGE/Climate. In the second simulation, the CO2 concentration is rising according to the A2 scenario in both the climate and vegetation models. The
Since most climate projections indicate future changes in climate variability (
Climate change might strongly modify the vegetation distribution in Europe. With constant (
The projected climate changes over the 21st century are likely to induce increased fire risk in the Mediterranean region but also in other parts of Europe. Indeed, in the fire-prone regions, an increase in air temperature and a reduction in summer rainfall are expected, although uncertainties exist about the exact precipitation change pattern.
As outlined in the Results section, runoff is generally underestimated by CARAIB over Europe. First, though the comparison deals only with grid cells covered by more than 30 % of natural vegetation (PCLM2000 map), the simulated potential natural vegetation, predominantly forests, may lead to runoff values different than the ones observed in a landscape patterned by human land use (crops areas, asphalt areas, etc.). Secondly, some features of hydrology in mountainous area,
NPP computed by CARAIB are in the range of the estimations obtained by various methods (field estimates and remote sensing products) but the model tends however to underestimate the high productivity values and to underestimate the lower ones. Note that with MODIS, according to
The comparison of biome distribution obtained with CARAIB with potential natural vegetation maps underlies some problems. In Ukraine and Iberian Peninsula, the discrepancies could arise from the water budget owing to possible inaccuracy in precipitation data, in calculation of evapotranspiration or in soil data and their relationship to water conductivity. Annual runoff in these two regions is correctly calculated by the model. These regions show contrasted soil water regimes. In the Iberian Peninsula, a drought reappears every summer but winter precipitations increase soil water above field capacity and thus allow the reconstitution of groundwater stocks. On the contrary, in Ukraine, the summer drought is not so severe and recurrent, but, some years, winter precipitations are not sufficient to raise soil water above the field capacity. This can occur during two to three successive years, preventing the refilling of groundwater reservoirs. In CARAIB, tree mortality owing to water stress only begins below a fixed soil water threshold, which depends on plant type. In addition, the response is assumed quite fast (characteristic time of approximately one month). In these conditions, the model does not allow trees to survive in south-western Iberian Peninsula where the computed soil water falls below the threshold. Actually, water transfers from groundwater to the root zone should occur, especially in valley area. In Ukraine, since groundwater is not refilled trees cannot survive. It seems necessary to refine further the modelling of the vertical and horizontal dynamics of soil and ground water stocks. This kind of problem seems to appear with other dynamic vegetation models. For instance, the LPJ model also predicts deciduous trees in southern Ukraine and C3 herbs in southern Spain as dominant plant functional types (
The discrepancy between simulated and observed area burned is that only natural fires due to lightning are considered in the model. Lightning causes less than 10 % of the fires, but are responsible for the largest burned areas (
The impacts of climate change and the potential CO2 effect on NPP of European forest ecosystems have been highlighted by two simulations with different CO2 concentration hypothesis (constant and rising concentrations). The real response of ecosystem to CO2 enrichment is however a question which is still discussed. It is argued that nutrient availability could be limiting on the primary productivity (
In accordance with the conclusions of
The factors expected to play the most significant role in the fire regimes during the 21st century are land-use and climate changes. The change in fire occurrence during the last decades closely reflects the recent socio-economic changes underway in many European countries, especially in the Mediterranean region, such as depopulation of rural areas, decreases in grazing pressure and wood gathering, increase in agricultural mechanization and tourism pressure, etc. (
In this paper, climate change impacts and potential CO2 fertilization effects on vegetation in Europe under the A2 ARPEGE/climate scenario have been illustrated through two simulations assuming constant and increasing CO2 concentration in the vegetation model. The A2 scenario was chosen because it corresponds to a rather important increase in atmospheric CO2 and thus to very substantial climate change leading to more extreme conditions for plants. The two simulations can be expected to bracket the future evolution of the system under an A2 ARPEGE/ Climate scenario, the actual path followed depending on the nutrient budget and the efficiency of the CO2 fertilization effects. Without CO2 fertilization, NPP might strongly decrease in many European areas except in the northern part. When CO2 fertilization is included, such decreases are not observed. However, in both cases, the simulated NPP shows increasing interannual fluctuations associated with more frequent and more severe summer droughts. These drier conditions might lead to an increasing fire risk and the annual burned area is projected to rise by a factor of 3 to 5 in the Mediterranean area compared to the present.
The study focused on the future evolution of vegetation represented by Bioclimatic Affinity Groups (BAGs). It shows that these BAGs will undergo significant change in productivity and eventually mortality associated with more severe and more frequent drought events in the future. Since they have a narrower bioclimatic spectrum, individual species are probably more vulnerable to climate change than BAGs. Consequently, it would be interesting to apply dynamic vegetation models at species level in order to analyse the response of a selected set of plant species to climate change. Dynamic vegetation models are indeed probably more appropriate tools to evaluate impacts of water stress on vegetation than niche-based models (
The response of European ecosystems to climate change has been studied assuming no dispersal limitations. The future species distribution depends, however, on the capacity of plants to migrate. Thus, the introduction of a dispersal module into CARAIB should allow studying more accurately the potential species shift and knowing if they could move fast enough to survive. This kind of question is certainly more relevant for herbs than for trees; the distribution of the latter being most of the time human managed.
Moreover, it would be worth to continue further the analysis by using the outputs of several climate models and several IPCC SRES scenarios to evaluate the uncertainties of climate projections and their impacts on future vegetation evolution. The analysis and the validation of climate model variability at the diurnal, seasonal and interannual time scales should be also carried out since climate variability at all these time scales will govern the response of plant species to climate change.
We thank Michel Déqué (MétéoFrance, CNRM, Toulouse) for providing the ARPEGE/Climate dataset and Dimitrios Efthymiadis (CRU, East Anglia) for the CRU TS3.0 dataset. Funding for this research from the ECOCHANGE integrated project (European Commission) and from the University of Liège (FSR 2010) is gratefully acknowledged.
Diagram illustrating the structure of the CARAIB model and summarizing its main input and output variables.
Annual runoff (mm yr-1) from: (a) CARAIB and (b) UNESCO (
Mean net primary productivity (g C m-2 yr-1) from: (a) CARAIB and (b) MODIS sensor for the 2000-2006 period for grid cells with ≥ 30 % natural vegetation; (c) NPP relative anomalies (%) between (a) and (b); (d) relationship between CARAIB NPP computed values and NPP MODIS estimated values for pixels occupied with ≥ 30 % natural vegetation for the 2000-2006 period (black points) or NPP field estimates collected between 1947 and 2005 (red squares -
Biome distribution computed by CARAIB for the 1981-2000 period.
Comparison of simulated and observed area burned (106 ha) in the Mediterranean region (
Mean net primary productivity (g C m-2 yr-1) computed by CARAIB with climate from (a) ARPEGE and (b) CRU for the 1981-2000 period. NPP relative anomalies (%) between 2081-2100 and 1981-2000 with (c) constant and (d) increasing atmospheric CO2 concentration conditions.
Standard deviation of net primary productivity (g C m-2 yr-1) computed by CARAIB for the 1981-2000 period with climate from (a) ARPEGE and (b) CRU and for the 2081-2100 period with (b) constant and (c) increasing atmospheric CO2 concentration conditions.
Soil water (annual mean, minimum and maximum monthly values) evolution for the 1961-2100 period and standard deviation (SD) computed by CARAIB for 14 consecutive years respectively for two grid cells in Greece (40 °N 22°E) and in Finland (66 °N 28°E). (a) evolution in Greece, (b) SD in Greece, (c) evolution in Finland, (d) SD in Finland. Soil water content is expressed in relative units,
Net primary productivity (g C m-2 yr-1) for the 1961-2100 period and standard deviation (SD) computed for 14 consecutive years respectively for two grid cells in Greece (40 °N 22°E) and in Finland (66 °N 28°E). (a) evolution in Greece, (b) SD in Greece, (c) evolution in Finland, (d) SD in Finland.
Biome distribution computed by CARAIB for the 2081-2100 period and biome difference map with regards to the 1981-2000 period under (a) and (c) constant and (b) and (d) increasing atmospheric CO2 concentration conditions.
Area burned (106 ha) in the Mediterranean Region (34° N to 44° N, 10° W to 40° E) over the 1961-2100 period computed by CARAIB (standard deviation SD computed for 14 consecutive years).
BAG-dependent parameters controlling plant stress and germination. Soil water thresholds SWmins and SWmaxg refer to available soil water in relative units,
N | BAG composition | Tmins (°C) | Swmins | GDD5ming (°C day) | Tmaxg (°C) | Swmaxg |
---|---|---|---|---|---|---|
1 | -41.2 | 0.036 | 497 | 2.8 | - | |
2 | -40.7 | 0.098 | 519 | 2.6 | - | |
3 |
|
-40.7 | 0.02 | 546 | 2.8 | - |
4 |
|
-50 | 0.02 | 50 | - | - |
5 |
|
-41.6 | 0.042 | 443 | 2.9 | - |
6 |
|
-25.9 | 0.127 | 1642 | 2 | - |
7 | -40.4 | 0.289 | 529 | -2.2 | - | |
8 |
|
-41.3 | 0.074 | 497 | 2.7 | - |
9 | -29.2 | 0.085 | 1307 | 1.6 | - | |
10 |
|
-41.3 | 0.093 | 558 | 2.7 | - |
11 |
|
-20.6 | 0.088 | 1458 | 2.2 | - |
12 |
|
-7.9 | 0.073 | 2677 | - | 0.383 |
13 |
|
-40.7 | 0.101 | 523 | 2.5 | - |
14 | -38.6 | 0.085 | 583 | 3.5 | - | |
15 |
|
-31.8 | 0.125 | 1153 | 1 | - |
16 |
|
-21.9 | 0.116 | 1602 | 1.4 | - |
17 |
|
-15.8 | 0.158 | 2006 | 1.5 | - |
18 |
|
-7.8 | 0.07 | 2695 | - | 0.466 |
19 |
|
-39.8 | 0.344 | 808 | -3.3 | - |
20 |
|
-41.5 | 0.095 | 555 | 2 | - |
21 |
|
-38.8 | 0.29 | 1048 | -4.1 | - |
22 |
|
-39.8 | 0.107 | 512 | 2.2 | - |
23 |
|
-22.4 | 0.522 | 550 | -7.3 | - |
24 |
|
-19.5 | 0.183 | 1472 | 0.1 | - |
25 |
|
-8.3 | 0.084 | 2537 | - | 0.415 |
Biome assignment scheme used in CARAIB. (GDD5): growing-degree-days above 5 °C cumulated over one year; (NPPtot): total NPP of the grid cell; (LAItot): total leaf area index of the grid cell (herbs + trees); (LAItree): leaf area index of the over-storey (trees); (R): NPP(herbs)/NPP(trees) = ratio of herb NPP (BAGs 1-12) to tree NPP (BAGs 13-25); (fbdec): cover fraction of temperate broadleaved deciduous trees in the overstorey; (fbev): cover fraction of temperate broadleaved evergreen trees in the overstorey; (fcold): cover fraction of boreal/temperate cold trees in the overstorey; (fndl): cover fraction of temperate needle-leaved trees in the overstorey; (fmed): cover fraction of Mediterranean trees in the overstorey; (fwarm):cover fraction of temperate warm trees in the overstorey.
N | Biomes | GDD5 (°C day) | NPPtot (g m-2 y-1) | LAItot | R | LAItree | Other conditions |
---|---|---|---|---|---|---|---|
1 | Ice | < 50 | - | - | - | - | - |
2 | Desert | ≥ 50 | < 10 | - | - | - | - |
3 | Semi-desert | > 700 | > 10 | < 0.3 | - | - | - |
4 | Tundra | 50-700 | > 10 | - | - | < 0.8 | - |
5 | Temperate grassland | > 700 | > 10 | ≥ 0.3 | > 0.4 | < 0.3 | - |
6 | Warm-temperate open woodland | - | > 10 | ≥ 0.3 | > 0.4 | ≥ 0.3 | fmed > 0.05 |
7 | Cold temperate/boreal open woodland | - | > 10 | ≥0.3 | > 0.4 | ≥ 0.3 | fmed ≤ 0.05 |
8 | Warm-temperate broadleaved evergreen forest | - | > 10 | ≥0.3 | ≤ 0.4 | - | fbev > 0.65 |
9 | Warm-temperate conifer forest | - | > 10 | ≥ 0.3 | ≤ 0.4 | - | fndl> 0.65 |
10 | Warm-temperate mixed forest | - | > 10 | ≥ 0.3 | ≤ 0.4 | - | fbev ≤ 0.65fndl ≤ 0.65fbdec ≤ 0.65fcold ≤ 0.8fwarm > 0.05 |
11 | Temperate broadleaved deciduous forest | - | > 10 | ≥ 0.3 | ≤ 0.4 | - | fbdec >0.65 |
12 | Cool-temperate mixed forest | - | > 10 | ≥ 0.3 | ≤ 0.4 | - | fbev≤ 0.65fndl ≤0.65fbdec ≤ 0.65fcold≤ 0.8fwarm≤ 0.05 |
13 | Boreal/montane forest | ≤ 1000 | > 10 | ≥ 0.3 | ≤ 0.4 | - | fcold > 0.8 |
Trend detection in CARAIB soil water and NPP time series. Auto-regressive (AR) and moving average (MA) effects to take into account serial dependence and p-values (significant trends (unit/yr) with *, see
Parameter | Statistics | AR | MA | Trend | p-value |
---|---|---|---|---|---|
Soil water in Greece | Minimum | - | - | -0.0005 | 0.02* |
Mean | - | 1 | -0.0011 | 0.00* | |
Maximum | 1 | 1 | -0.0011 | 0.00* | |
Soil water in Finland | Minimum | - | - | - | 0.89 |
Mean | - | - | - | 0.94 | |
Maximum | - | - | 0.0005 | 0.00* | |
NPP in Greece | Constant CO2 | - | 1 | -1.73 | 0.00* |
Increasing CO2 | - | 1 | 2.26 | 0.00* | |
NPP in Finland | Constant CO2 | - | - | 1.04 | 0.00* |
Increasing CO2 | - | - | 2.68 | 0.00* |
Trend detection in variability of CARAIB soil water and NPP time series. Spearman rank coefficient (R) between time and standard deviation computed for 14 consecutive years and p-values (n = 10, significant trend with tagged with an asterisk).
Parameter | Statistics | R | p-value |
---|---|---|---|
Soil water in Greece | Minimum | 0.2121 | 0.56 |
Mean | 0.6242 | 0.05* | |
Maximum | 0.7333 | 0.02* | |
Soil water in Finland | Minimum | 0.1515 | 0.68 |
Mean | 0.1152 | 0.75 | |
Maximum | -0.4788 | 0.16 | |
NPP in Greece | Constant CO2 | 0.1636 | 0.65 |
Increasing CO2 | 0.7939 | 0.01* | |
NPP in Finland | Constant CO2 | -0.697 | 0.03* |
Increasing CO2 | -0.3576 | 0.31 |