Frequently Asked Questions

Climate change

An increase in average temperatures in France of 1.4°C has been observed since 1900, the cumulative rainfall remains stable across the country but contrasting changes appear depending on the region and the season, the duration of snow cover is decreasing in mountains and the intensity of the droughts is increasing. Since 1980, the increase in warming has been significant, with an average rate of around +0.3°C each decade (source: Climat HD).

On the scale of France, annual precipitation has not shown any marked change since 1961. However, it is characterized by a clear disparity with an increase over a large northern half (especially the northeast quarter) and a decrease in the south of the country (source: Climat HD).

The analysis of the annual percentage of the surface affected by soil drought since 1959 makes it possible to identify the years having experienced the most severe events such as 1976, 1989, 2003 and 2011. The evolution of the ten-year average shows the increase of the surface of droughts going from values the order of 5% in the 1960s to more than 10% today (source: Climat HD).

Heat waves recorded since 1947 on a national scale have been significantly more numerous in recent decades. This development is also materialized by the occurrence of longer and more severe events in recent years. Thus, the three longest heat waves and three of the four most severe episodes occurred after 2000. The heat wave observed from August 2 to 17, 2003 was by far the most severe in France. It was also during this episode and during the heat wave from July 21 to 26, 2019 that the hottest days since 1947 were observed (source: Climat HD).

The number of frost days observed in France is quite different depending on the region and shows strong variations from one year to the next. Over the period 1961-2010, a decrease is observed in all regions: the decreases are less marked in the coastal areas where the annual number of frost days is low, the strongest decreases are observed in the northeast and the center of the country; in the other regions, the drop is between two and four days per decade (source: Climat HD).

Climate projections

Numerical climate models are used to project the possible future course of the climate system as well as to understand the climate system itself. They are built on mathematical descriptions of the governing physical processes of the climate system (eg, momentum, mass and conservation of energy, etc.).

The climate is a synthetic representation of climatic variables characterizing a given region. It is defined by the average values, generally over 30 years (according to the World Meteorological Organization), and the dispersion around the average of climatic quantities (temperature, rainfall, wind, sunshine, etc.) and particular phenomena such as fog, storms, hail. Conversely, the notion of "weather" refers to the meteorological conditions of a given moment or a short period (a day, a week, etc.).

The user can use 3 different time periods in the application: the Recent Past (1985 to 2020), the Near Future (2021-2050) and finally the Far Future (2051-2100). The principle of CANARI is to compare the representation of an indicator between these different time periods to understand what the evolutions are: Recent Past / Near Future, or Recent Past / Near Future / Far Future. Initially, it is advisable to promote comparisons of Recent Past / Near Future indicators that are easier to analyze and explain to farmers. It is also often easier for an economic player to limit himself to the next 30 years to structure his adaptation process.

The user can choose two different RCP (for Representative Concentration Pathway) scenarios in CANARI: the RCP 4.5 scenario (or intermediate greenhouse gas emissions reduction target scenario) and the 8.5 scenario (or extreme or pessimistic scenario, absence of a greenhouse gas emission reduction target). Due to climate inertia, the choice of RCP 4.5 or 8.5 will have little impact on the results for the Near Future period (2021-2050). On the other hand, the choice of the RCP 4.5 or 8.5 scenario will be decisive for the calculation of an indicator for the Far Future period (2051-2100).

CANARI offers the user results for a set of 6 different pairs of simulations. There are two main sources of uncertainty concerning climate projections: the "model" uncertainty linked to the representation of physical processes and the uncertainty associated with greenhouse gas emission scenarios whose effect is significant above beyond 2050. It is therefore recommended to systematically use simulations from several models in order to foresee the possible variability of the results.

The CANARI portal offers the user only climate projections, so there are no climate observations. The values proposed for the Recent Past period in CANARI therefore correspond to climate simulations specific to each of the selected models. However, bias correction methods were applied to the simulations taking into account observations.

Agricultural impacts

The last two decades have seen the decline of the upward trend in cereal yield in many European countries, including France. Climate change (heat stress, drought) is one of the major explanatory factors for the stagnation of yields. Recently, the year 2016 completes the list of years where the climate has severely affected yields. Thus, farms must deal with greater interannual variability in yield.

With CANARI, it is possible, for example, to quantify and visualize the evolution of the risk of heat stress (scalding) or water deficit at different periods of the wheat development cycle.

Since the 1980s, the harvest date has been brought forward by nearly 20 days for most vineyards in France. This progress is due to the average increase in temperatures of about 0.3°C. per decade (i.e. 1.2°C. over a period of 30 years). Thus, the harvest takes place during a warmer period, with consequences on the quality of the wine (degree of alcohol, aromatic profiles, etc.). The higher accumulation of temperatures also results in an earlier start to vegetative growth in the spring, with the consequent exposure to the risk of frost. Finally, the productive potential of the vine is regularly constrained by a growing water deficit, particularly in the southernmost terroirs of France.

With CANARI, it is possible, for example, to quantify and visualize the evolution of the risk of late frost for the vine, the evolution of the water deficit over the crop cycle or even the evolution of the thermal availability in relation to each grape variety.

Like all cultivated plants, fodder species also benefit from a higher accumulation of degree days with the rise in average temperature in France. This is particularly the case for meadows, the dates for which vegetation resumes, animals are put out to grass, or silage or hay is made tend to advance by several days. The same is true for annual fodder species such as silage corn, the harvest date of which is advancing regularly. In addition, the strengthening of the water deficit over the spring and summer periods causes reductions in the production of fodder to feed the animals.

With CANARI, it is possible, for example, to quantify and visualize the evolution of the dates of recovery of grasslands (grassing, hay, etc.), or even the evolution of the summer water deficit of grasslands or corn silage.

The cow is poorly adapted to heat since it evacuates it with difficulty by sweating little while it produces a lot itself. Cows show their discomfort in the event of heat stress by visible changes in their behaviour: they stay up longer, seek shade and watering points, going so far as to reduce their food metabolism and consequently their level of production of milk. Thus, a moderate heat wave (5 consecutive days at more than 30°C.) can lead to a drop of 20 to 30% in daily milk production. The issue of heat stress in cows is now a central concern for all breeders.

With CANARI, it is possible, for example, to quantify and visualize the risks of thermal stress according to the time of year, or even to determine the level of thermal discomfort through the different classes of THI (Temperature-Humidity Index).

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