WEBVTT 00:23.910 --> 00:29.850 Let's begin for the second part of this lecture on biophysical effects 00:29.850 --> 00:32.910 of land use and land cover changes. 00:33.690 --> 00:41.330 But just before, let's recap a little the major biophysical changes 00:41.330 --> 00:45.270 that land use provokes. 00:46.270 --> 00:52.410 Three major biophysical changes through the albedo, the roughness and 00:52.410 --> 00:55.010 the evapotranspiration efficiency. 00:55.810 --> 01:02.710 We have seen that in general, when you deforest a dark forest, then 01:02.710 --> 01:08.250 you have a brighter surface, more reflected sunlight, so cooler 01:08.250 --> 01:08.890 temperature. 01:08.890 --> 01:13.910 That's a radiative effect, a cooling effect, but you have also two 01:13.910 --> 01:18.110 other effects that tend to warm the surface relatively. 01:19.730 --> 01:24.670 When you cut a forest, then you decrease the roughness length and then 01:24.670 --> 01:30.710 you decrease the capacity of the surface to exchange turbulent fluxes 01:30.710 --> 01:31.370 with the atmosphere. 01:31.370 --> 01:34.610 So you increase the surface temperature. 01:35.770 --> 01:41.550 And also, forests have efficient routes to extract soil moisture, 01:42.390 --> 01:45.590 which is not the case when you cut the forest. 01:46.190 --> 01:51.090 And then that leads to higher surface temperature. 01:52.690 --> 01:58.570 We have seen that the relative contribution of these biophysical 01:58.570 --> 02:01.110 changes are different among the latitudes. 02:01.810 --> 02:08.230 The predominant effect at boreal latitude is the albedo cooling due to 02:08.230 --> 02:09.090 deforestation. 02:10.690 --> 02:15.190 That dominates entirely the roughness and evapotranspiration 02:15.190 --> 02:20.690 efficiency changes, which leads to a small cooling effect over 02:20.690 --> 02:21.730 deforested area. 02:23.390 --> 02:31.010 And it's not the case around tropical deforested areas, because albedo 02:31.010 --> 02:32.410 contrast is not so high. 02:32.890 --> 02:37.570 You don't have the snow mask effect as in boreal latitudes. 02:38.570 --> 02:43.430 And the roughness and evapotranspiration efficiency, that are quite 02:43.430 --> 02:47.910 high in tropical forests, dominates the albedo cooling. 02:48.270 --> 02:53.450 So you have this small warming effect due to deforestation. 02:55.410 --> 03:04.030 So let's see now, there was a question yesterday about deforestation, 03:04.270 --> 03:06.390 but what about afforestation? 03:08.130 --> 03:15.070 And what is the net impact of northern hemisphere afforestation? 03:16.790 --> 03:24.330 So that's a study from Betz in Nature 2000, comparing the 03:24.330 --> 03:32.650 biogeochemical effects of afforestation, which leads to a higher 03:32.650 --> 03:37.310 carbon uptake, as we can see there in the top panel. 03:37.310 --> 03:45.710 So Betz put dense coniferous forests at each grid cell, so it gives 03:45.710 --> 03:49.250 more carbon uptake for boreal forests, for example. 03:50.870 --> 04:02.070 But you have a biogeophysical adverse effect, that is the decrease in 04:02.070 --> 04:05.590 albedo, because forests are dark. 04:06.560 --> 04:15.170 So what is the additive effect, what is the net impact of temperate 04:15.170 --> 04:16.210 afforestation? 04:17.490 --> 04:24.250 So it expressed that in terms of net global relative forcing, albedo 04:24.250 --> 04:28.350 from one part, CO2 sequestration from the other part. 04:28.950 --> 04:37.770 And the yellow grid cells, orange and red, denotes positive net 04:37.770 --> 04:46.550 forcings, that implies that these regions, when you afforest, that 04:46.550 --> 04:48.070 cause a net warming. 04:49.070 --> 04:54.730 You have also some regions that experiment neutral effects of 04:54.730 --> 05:00.270 afforestation, so the carbon sequestration is balanced by the decrease 05:00.270 --> 05:05.070 in albedo, like Russian regions. 05:05.870 --> 05:11.330 And you have some regions that still see, like North America and 05:11.330 --> 05:16.310 Europe, some negative forcings, so cooling net impacts of 05:16.310 --> 05:17.130 afforestation. 05:17.310 --> 05:21.770 But of course smaller than that expected from only, if you take into 05:21.770 --> 05:24.270 account the biogeochemical effects. 05:24.670 --> 05:30.490 So this is why biogeophysical impacts are important to take into 05:30.490 --> 05:30.870 account. 05:31.970 --> 05:39.370 This is a summary of the existing literature of biophysical impacts of 05:39.370 --> 05:42.210 deforestation on temperature and precipitation. 05:43.050 --> 05:48.910 So that's a compilation I made on more than 40 modeling and 05:48.910 --> 05:54.750 observational studies among the regions, we will go step by step. 05:56.270 --> 06:02.070 This part denotes the biophysical effects on both temperature and 06:02.070 --> 06:02.650 precipitation. 06:03.330 --> 06:07.910 If you have arboreal deforestation, if you have a temperate 06:07.910 --> 06:10.850 deforestation, if you have a tropical deforestation, if you have a 06:10.850 --> 06:11.750 global deforestation. 06:13.610 --> 06:17.350 And there you have the net effects, so the biophysical, more the 06:17.350 --> 06:21.830 biogeochemical effects due to deforestation. 06:23.810 --> 06:28.970 So what's interesting is that in general the study agreed that if you 06:28.970 --> 06:34.670 deforest the global, sorry, the boreal forests, you will have a 06:34.670 --> 06:40.770 cooling effect due to the albedo drastic increase with the snow 06:40.770 --> 06:41.990 globally. 06:43.070 --> 06:49.710 A small cooling in the case of temperate deforestation, which can be 06:49.710 --> 06:51.180 seen at global scale. 06:52.830 --> 06:58.310 But see that among three or four studies that made some tropical 06:58.310 --> 07:05.290 deforestation, you just have an unchanged or very small cooling 07:05.290 --> 07:08.470 globally on the temperature. 07:09.470 --> 07:15.910 Because you have a large increase of temperature regionally on the 07:15.910 --> 07:19.750 deforested area in the tropics, but it's balanced by the extra 07:19.750 --> 07:22.630 -tropical cooling in general. 07:23.910 --> 07:28.550 And for global scale deforestation, I showed for example the study of 07:28.550 --> 07:36.510 Dava and Denoble, you still have a cooling global effect. 07:37.410 --> 07:43.370 If you add to this biophysical effect the biogeochemical effects, 07:43.770 --> 07:50.310 which is warming in general for deforestation, you lose carbon 07:50.310 --> 07:52.290 sequestration possibility. 07:53.190 --> 07:58.590 For boreal, it's not so high because boreal forests have not so high 07:58.590 --> 08:04.130 carbon content compared to tropical forests that really increase the 08:04.130 --> 08:04.590 temperature. 08:04.590 --> 08:11.250 So the net effect, the felt effect of a tropical deforestation at 08:11.250 --> 08:16.110 present day would be on global temperature, a warming. 08:16.730 --> 08:21.970 Whereas we will have a global cooling for boreal deforestation. 08:22.110 --> 08:26.750 We still don't know for temperate, it depends on how you define 08:26.750 --> 08:30.430 temperature regions, which band of latitude also. 08:31.290 --> 08:39.770 Now for regional aspects, so what is the impact of boreal 08:39.770 --> 08:42.510 deforestation on boreal regions? 08:42.670 --> 08:47.870 You still have the cooling because of the albedo dramatic increase. 08:48.710 --> 08:54.110 For tropical areas, when you deforest especially the Amazon, not so 08:54.110 --> 09:03.390 Africa or Southern Asia, you have a really good increase that is 09:03.390 --> 09:06.970 enhanced with the net biogeochemical effect. 09:07.150 --> 09:15.730 So you can reach some 2 degrees locally on annual mean temperature 09:15.730 --> 09:16.710 regionally. 09:18.530 --> 09:24.150 For precip now, you can see that there is a lot of interrogations 09:24.150 --> 09:30.050 because we don't know, it's model dependent, whether the precip 09:30.050 --> 09:33.450 globally are modified or not. 09:33.450 --> 09:38.230 It seems that in general, when you cut forests globally, boreal, 09:38.510 --> 09:44.810 temperate, etc., you reduce the evapotranspiration efficiency of all 09:44.810 --> 09:47.730 the previous forests. 09:48.070 --> 09:53.530 And so you have less atmospheric water vapor in the atmosphere, so 09:53.530 --> 09:56.450 less probability to form precipitation. 09:57.150 --> 10:05.950 This is why in general we have this minus sign, so less precipitation 10:05.950 --> 10:08.590 both globally and regionally. 10:08.590 --> 10:14.850 When you had the small warming due to biogeochemical effect, then it's 10:14.850 --> 10:21.170 all unchanged or unknown, except for tropical regions where you have 10:21.170 --> 10:21.870 drying. 10:21.990 --> 10:26.590 It's here in blue, but it's a reduction of precipitation regionally. 10:28.310 --> 10:34.890 So tropical deforestation leads to warmer and drier regional climates. 10:38.170 --> 10:46.470 OK, so I've just seen some experiments, observational studies also on 10:46.470 --> 10:49.090 deforestation, afforestation. 10:49.850 --> 10:56.230 But what about the existing, the experimented land use and land cover 10:56.230 --> 10:57.990 change in the past decades? 10:58.250 --> 11:03.070 What is the biophysical impacts of these land use and land cover 11:03.070 --> 11:03.530 changes? 11:03.530 --> 11:09.670 And we will see after how the future land use and land cover changes 11:09.670 --> 11:14.690 will impact the temperature through biophysical impacts. 11:16.250 --> 11:21.470 So to study the biophysical of historical land use and land cover 11:21.470 --> 11:29.850 change, you need to isolate the land use impacts of the past century. 11:29.890 --> 11:34.050 Because you have simulation with land use, with volcanic eruption, 11:34.410 --> 11:38.070 with solar irradiance changes, with CO2 increase. 11:38.950 --> 11:47.750 And what basically the Lucid intercomparison exercise made is asking a 11:47.750 --> 11:56.210 lot of global coupled models to make simulation without land use. 11:56.630 --> 12:00.850 And then the difference with and without land use will give you the 12:00.850 --> 12:06.310 contribution of the historical land use and land cover change. 12:06.310 --> 12:11.670 So have you seen, you maybe have seen the crop and pasture fraction 12:11.670 --> 12:19.310 difference simulated here between 1992 and 1870. 12:20.910 --> 12:26.950 We have a large increase in crop and pasture fraction and a large 12:26.950 --> 12:32.730 decrease in forest fraction, especially in North America and in 12:32.730 --> 12:34.330 Eurasia. 12:36.510 --> 12:45.610 So these are the results for seven models in lines for the impact of 12:45.610 --> 12:50.730 historical land use and land cover changes on latent heat flux, on 12:50.730 --> 12:56.430 surface temperature, and on the third column is precipitation. 12:57.450 --> 13:02.810 So for the first column we see that we have both red pixels that 13:02.810 --> 13:10.290 denote decrease in latent heat flux and some models with blue pixels 13:10.290 --> 13:13.410 denoting increase in latent heat flux. 13:13.570 --> 13:19.070 So we have a large uncertainty among the models, among the Lucid 13:19.070 --> 13:24.150 models concerning the evapotranspiration changes due to land use and 13:24.150 --> 13:25.030 land cover changes. 13:26.910 --> 13:35.410 For the temperature now, we in general, except maybe for this IPSL 13:35.410 --> 13:42.650 model, most models agree on blue pixels, so cooling effect due to past 13:42.650 --> 13:46.870 land use and land cover change in the last century. 13:48.470 --> 13:55.730 And for precipitation, where very few pixels are significant, and we 13:55.730 --> 14:00.330 both have red pixels, blue pixels, so no clear signal for 14:00.330 --> 14:00.970 precipitation. 14:01.670 --> 14:06.850 We only seem to have a signal for temperature. 14:07.850 --> 14:14.510 So now when we compare the global values of temperature due to 14:14.510 --> 14:18.970 biophysical land use and land cover changes of the past century, for 14:18.970 --> 14:26.970 each model we have a cooling effect of on average minus 0.02 degrees. 14:27.690 --> 14:37.750 If we compare it to the CO2 emissions that lead to a warming on the 14:37.750 --> 14:41.690 past century, it's more about 0.4 degrees. 14:42.410 --> 14:48.070 So that's one order of magnitude less for the biophysical impacts of 14:48.070 --> 14:53.130 land use and land cover change of the past century for global 14:53.130 --> 14:53.950 temperature. 14:53.950 --> 15:01.370 That means that the trends in global warming for the past century were 15:01.370 --> 15:10.150 slightly reduced at maximum 10% due to the land use and land cover 15:10.150 --> 15:11.970 changes globally. 15:12.970 --> 15:19.550 Now regionally, if we make the same exercise for surface temperature, 15:20.130 --> 15:25.830 comparing land use and land cover changes here with dark grey boxes 15:25.830 --> 15:33.530 for the North America region at each season, and we compare this 15:33.530 --> 15:40.110 biophysical impact of land use and land cover change with the warming 15:40.110 --> 15:45.830 due to CO2 and SST sea surface temperature increase, we have in fact 15:45.830 --> 15:49.970 comparable impacts regionally on temperature. 15:50.530 --> 15:59.970 So biophysical impacts are as much as important as CO2 increase on 15:59.970 --> 16:00.390 temperature. 16:01.610 --> 16:06.810 Now what's interesting on this figure is also the large spread between 16:06.810 --> 16:08.590 the models. 16:09.750 --> 16:17.290 So they try to attribute in this paper and in others the large spread 16:17.290 --> 16:20.130 on temperature simulated. 16:21.130 --> 16:30.390 A first possibility is because the implementation of natural 16:30.390 --> 16:32.810 vegetation for each model is different. 16:33.490 --> 16:36.990 The implementation of non-crop and pasture maps are different. 16:37.430 --> 16:41.350 That leads to different land cover maps for each model. 16:42.070 --> 16:48.650 And in absolute value, you have differences in forest extent simulated 16:48.650 --> 16:49.650 by each model. 16:50.130 --> 16:55.110 Look at, for example, for ECRF model that simulates for the past land 16:55.110 --> 17:00.890 use and land cover change, minus 40% in North America forest extent, 17:01.230 --> 17:05.270 whereas for ECM5 it's only maybe minus 10%. 17:05.270 --> 17:12.510 So that contributes to the model spread we saw on temperature. 17:13.510 --> 17:23.510 A second thing is the low agreements among the lucid models concerning 17:23.510 --> 17:27.690 the changes in the partitioning of latent and sensible heat flux. 17:28.370 --> 17:35.570 So we see there for each model the changes in latent heat flux 17:35.570 --> 17:40.470 normalized per forest fraction changes. 17:41.790 --> 17:49.010 So for a given fraction of forest change, we have for example this 17:49.010 --> 17:53.330 model that displays no sensitivity to land use and land cover change, 17:53.450 --> 17:56.630 just a small decrease in latent heat flux. 17:57.590 --> 18:03.570 We have this model with a moderate sensitivity, but that simulates an 18:03.570 --> 18:06.870 increase in latent heat flux when you deforest. 18:08.030 --> 18:11.450 Which is not the intuitive paradigm. 18:13.090 --> 18:17.230 And this one, IPSL, a very large sensitivity to land use and land 18:17.230 --> 18:24.410 cover change, that simulates a latent heat flux decrease when the 18:24.410 --> 18:25.650 grasslands are increasing. 18:26.910 --> 18:32.570 So you have a large diversity of models with large diversity of 18:32.570 --> 18:38.530 sensitivity of evapotranspiration changes due to land use and land 18:38.530 --> 18:39.050 cover change. 18:39.270 --> 18:42.170 That can explain the spread in temperature simulated. 18:44.570 --> 18:51.350 I didn't talk about the urbanization impacts on climate. 18:52.030 --> 18:57.170 That can also, when you have urbanized area, we saw it for the water 18:57.170 --> 19:02.610 cycle for example, with less infiltration, we have a lot of 19:02.610 --> 19:03.950 biophysical changes. 19:03.950 --> 19:11.110 So you can think that there is an impact of urbanization on 19:11.110 --> 19:12.530 temperature changes. 19:13.570 --> 19:18.690 In fact, we denote the urban Italy as a relative warmth of a city 19:18.690 --> 19:21.850 compared to surrounding rural areas. 19:22.470 --> 19:27.990 And the biophysical changes due to urbanization depend of course on 19:27.990 --> 19:29.570 the replaced land cover. 19:30.170 --> 19:34.590 If before you had crops or you had natural vegetation. 19:35.990 --> 19:41.810 You increase the surface runoff, you have more heat retention, mostly 19:41.810 --> 19:43.070 because of the concrete. 19:43.870 --> 19:49.930 So high sensible heat flux and absolutely low evapotranspiration. 19:49.930 --> 19:54.370 You have also changes in albedo, so it has to be studied. 19:55.130 --> 20:00.130 And in general, what we see is that you have a strong local warming 20:00.130 --> 20:05.430 from 2 to more than 6 degrees depending on the size of the city and 20:05.430 --> 20:07.630 the greenness of the city. 20:08.630 --> 20:17.910 But if you look at a global scale, here we have the trends in 20:17.910 --> 20:25.410 temperature at rural stations in green from the past decades, so we 20:25.410 --> 20:26.570 see the warming. 20:28.210 --> 20:33.370 And in brown, we have the same trends in temperature at urban 20:33.370 --> 20:34.010 stations. 20:34.790 --> 20:41.030 And we see very little difference, so there is almost no impact of 20:41.030 --> 20:43.690 urbanization on global warming trends. 20:44.570 --> 20:49.270 On the past decades, we can see that in urban areas, we have a very 20:49.270 --> 20:51.610 slight warming here. 20:51.870 --> 20:53.690 The brown curve is above. 20:54.910 --> 21:00.530 Of course, we can think about some mitigations for these strong local 21:00.530 --> 21:02.990 warming effects, like white roofs. 21:03.110 --> 21:09.590 I don't present it there, but white roofs increase the albedo, so it 21:09.590 --> 21:13.030 decreases the temperature in surface. 21:14.370 --> 21:18.790 And we can also think of greener cities, so green areas, forests in 21:18.790 --> 21:19.570 the cities. 21:19.570 --> 21:26.270 And in general, the prevailing paradigm is that when you increase the 21:26.270 --> 21:34.190 greenness, as here, EVI is an index of greenness, you decrease the 21:34.190 --> 21:38.870 daytime temperature in the city, and quite strongly. 21:39.830 --> 21:45.330 So, the prevailing paradigm is that the greener, the cooler for the 21:45.330 --> 21:46.610 urban island. 21:49.450 --> 21:56.150 Now, I talked about effects on temperature and precipitation due to 21:56.150 --> 22:00.510 land use and land cover change, but what about wind energy or wind 22:00.510 --> 22:00.950 turbines? 22:01.310 --> 22:07.730 Are they affected by the roughness changes due to deforestation or to 22:07.730 --> 22:08.470 afforestation? 22:09.470 --> 22:11.910 So, that's a study from Votar et al. 22:11.950 --> 22:18.450 in Nature Geoscience 2010, showing that over the past 30 years, here 22:18.450 --> 22:27.010 it's the trend in wind speeds in meters per second per decade, we have 22:27.010 --> 22:33.270 blue spots that denote a decreasing trend of northern hemisphere wind 22:33.270 --> 22:35.650 speeds of about 10%. 22:35.650 --> 22:41.870 And they try to attribute this decreasing trend in northern hemisphere 22:41.870 --> 22:45.930 wind speeds due to roughness lands. 22:46.970 --> 22:53.350 So, they looked at meteorological stations and when the NDVI was 22:53.350 --> 23:00.900 available for spring-summer, so an index of vegetation contents, you 23:00.900 --> 23:06.100 see that when you increase the NDVI, you decrease the wind speed 23:06.100 --> 23:06.640 trends. 23:06.840 --> 23:14.060 So, you have less wind in general when you increase the vegetation. 23:14.720 --> 23:20.160 So, that's completely consistent with the increasing roughness that's 23:20.160 --> 23:22.420 reduced the wind speed. 23:22.420 --> 23:29.080 And that's also what denotes this modeling experiment of simulated 10 23:29.080 --> 23:35.120 -meter wind speed anomalies due to an increase of 50% of the roughness 23:35.120 --> 23:42.380 parameter in the MM5 regional model in the Eurasian region. 23:42.500 --> 23:49.020 So, you have blue, it denotes the reduction of wind speed due to 23:49.020 --> 23:49.620 deforestation. 23:49.620 --> 23:52.980 And especially in Russia, but also Europe and China. 23:53.140 --> 23:59.840 So, that's a concern of afforestation, reforestation, deforestation 23:59.840 --> 24:02.740 also. 24:04.960 --> 24:12.100 Let's see now what are the biophysical impacts of future land use and 24:12.100 --> 24:13.120 land cover changes. 24:16.380 --> 24:28.060 Davental in general 2007 made one IPSL simulations to see the impact 24:28.060 --> 24:32.460 of historical, so on the left part, historical land use and land cover 24:32.460 --> 24:34.580 change on temperature and on future. 24:34.580 --> 24:39.860 What we see is that we have still this past cooling effect of land 24:39.860 --> 24:45.700 use, and it seems to be a little increased, especially over North 24:45.700 --> 24:57.220 America and Russia, with this strong warming due to the scenario of 24:57.220 --> 24:59.740 tropical Amazon deforestation. 24:59.740 --> 25:04.080 So, still a cooling, but that's one model. 25:05.080 --> 25:12.120 There was also this Lucid SEMI5 experiment, so it's basically the same 25:12.120 --> 25:21.520 as Lucid, but for the future, using six SEMI5 models with the scenario 25:21.520 --> 25:30.040 RCP 8.5, and so simulation with and without land use and land cover 25:30.040 --> 25:30.940 change for the future. 25:31.800 --> 25:36.800 And these are the results there in the second column for changes in 25:36.800 --> 25:37.960 surface air temperature. 25:38.780 --> 25:43.640 So, in general, the simulated biophysical and net effects of future 25:43.640 --> 25:47.120 land use change are globally not significant. 25:48.400 --> 25:51.540 Both for temperature, but also for precipitation. 25:52.600 --> 25:57.480 Some regions, of course, with more than 10% of land use and land cover 25:57.480 --> 26:02.780 change for the future have significant, but it's not robust, the 26:02.780 --> 26:04.700 changes among the models. 26:04.700 --> 26:12.800 We can have higher signals for regional patterns or extremes, for 26:12.800 --> 26:14.780 example, for hot extremes. 26:16.860 --> 26:24.900 So, this is a talkative picture from Nathalie Denoblé, showing the 26:24.900 --> 26:30.760 potential relative roles of biophysical versus the biochemical effects 26:30.760 --> 26:32.220 you saw with Joe. 26:32.220 --> 26:37.140 Of land use and land cover changes, so at global scale now, the 26:37.140 --> 26:42.240 biogeophysical effects are quite balanced with the biogeochemical 26:42.240 --> 26:43.600 effects. 26:44.160 --> 26:50.220 At regional scale, they can have really strong effects nowadays. 26:51.560 --> 26:57.660 The land use is dominating, in some cases, the warming trend due to 26:57.660 --> 26:58.040 CO2. 26:58.990 --> 27:06.520 But in future, the biogeochemical effects remain stronger. 27:07.860 --> 27:13.600 But for regional studies, it's still as much as important as 27:13.600 --> 27:17.360 biogeochemical impacts for the future. 27:20.880 --> 27:30.360 I will show now some more specific decisions of land use change for 27:30.360 --> 27:31.540 climate mitigation. 27:34.000 --> 27:42.600 For example, the tillage effect can change biophysical property, as 27:42.600 --> 27:47.460 you can see in this photo of a non-tilled field here in green. 27:48.300 --> 27:55.640 And a tilled field, which is, as you can see, darker. 27:56.680 --> 28:00.200 So you see immediately there is absolutely no change in roughness 28:00.200 --> 28:01.360 length, or very few. 28:02.740 --> 28:07.320 So the major change in biophysical property is really the albedo. 28:07.500 --> 28:12.000 So what is the impact of the tillage in Europe? 28:12.000 --> 28:15.320 This is a recent study from Davan et al. 28:15.400 --> 28:17.760 in Nature, climate change in 2014. 28:18.980 --> 28:21.760 And this is exactly what we see there in the curve. 28:22.880 --> 28:25.740 In blue, we have no tillage, so this field. 28:27.660 --> 28:35.220 And we see that with tillage, we have darker albedo, so low albedo, 28:35.540 --> 28:38.020 with a difference of 0.1. 28:38.020 --> 28:44.080 So what is the impact on temperature due to tillage? 28:45.080 --> 28:51.360 In fact, here is the impact of if you don't practice the tillage in 28:51.360 --> 28:57.860 all Europe, and you have a cooling effect due to the practice of non 28:57.860 --> 28:58.400 -tillage. 28:58.400 --> 29:04.560 And especially for the highest percentile of Tmax, so it can really 29:04.560 --> 29:09.690 inhibit substantially heat waves development. 29:10.360 --> 29:12.910 For example, hot extremes locally. 29:16.410 --> 29:23.690 Another specific decision that took place in Florida in 1993, where 29:23.690 --> 29:29.170 they decided to plant lemon crops, sugarcane and winter wheat in 29:29.170 --> 29:34.670 Florida because they were thinking that it's warmer, so it will be 29:34.670 --> 29:35.970 good for the crops. 29:36.510 --> 29:41.930 But of course, you modify the land use and the land cover. 29:42.910 --> 29:45.150 So what happens locally? 29:45.830 --> 29:52.130 They wanted to avoid devastating freezes, so here it's the vegetation 29:52.130 --> 29:56.650 predominating at the beginning of the century. 29:58.760 --> 30:02.990 So with a lot of forests, with swamps also. 30:04.870 --> 30:11.850 And after the decision in 1993, so in yellow all the crops, and they 30:11.850 --> 30:13.510 drained the swamps also. 30:14.930 --> 30:16.990 So Marshall et al. 30:17.070 --> 30:24.170 in Nature 2003 studied, with an atmospheric regional model, the 30:24.170 --> 30:34.530 effects of this land use change, making a simulation post-1993 and pre 30:34.530 --> 30:35.710 -1993. 30:36.710 --> 30:44.530 And, oops, because what we see there is the temperature changes after 30:44.530 --> 30:50.550 the decision, and we have a cooling effect around the drained swamps, 30:51.630 --> 30:57.530 with an increase in the duration of frozen days, so that's the frozen 30:57.530 --> 30:59.170 length period. 31:00.170 --> 31:05.090 So that completely leads to the contrary of what they previously 31:05.090 --> 31:05.540 thought. 31:06.170 --> 31:07.070 Why? 31:07.790 --> 31:12.540 Because with natural swamps we have a minimum of evaporation flux. 31:13.770 --> 31:19.030 This evaporation leads to a moistening of the boundary layer, and this 31:19.030 --> 31:21.350 atmospheric water vapor is a greenhouse gas. 31:21.350 --> 31:27.270 So in this column, the presence of the water vapor seems to avoid 31:27.270 --> 31:31.530 temperatures to go below zero degrees when you had swamps. 31:32.830 --> 31:39.090 So with modeling studies, this is typically the decisions you can 31:39.090 --> 31:44.830 assess, and maybe these adverse effects could have been prevented. 31:47.190 --> 31:49.170 Irrigation effects. 31:50.570 --> 31:59.330 Irrigation drastically increased in the past century, especially in 31:59.330 --> 32:05.090 Asia, where you concentrate 70% of irrigated areas in the world. 32:06.090 --> 32:14.370 So Puma and Cook in GJR 2010 tested the effects of irrigation, the 32:14.370 --> 32:20.250 impacts of irrigation at global scale, on the surface temperature. 32:20.250 --> 32:28.010 So here it's only for summer, and we see that we have a summer cooling 32:28.010 --> 32:34.170 due to the fact that with irrigation you provide more water to the 32:34.170 --> 32:38.770 soil, you have more annual evaporation, precipitation and cloud cover 32:38.770 --> 32:42.930 increase, and in general you have less annual sensible heat flux that 32:42.930 --> 32:43.750 warms the surface. 32:43.990 --> 32:46.810 So you have this cooling effect. 32:49.210 --> 32:56.190 You can also think about the water management effects, especially in 32:56.190 --> 32:56.750 Europe. 32:57.050 --> 33:00.510 This is a study we made in 2012. 33:01.650 --> 33:07.590 That's an observational study for the 60 past years, where you have 33:07.590 --> 33:12.130 here the percentage of hot days in continental Europe, in function of 33:12.130 --> 33:17.010 an index of the soil moisture contained in soil at the beginning of 33:17.010 --> 33:17.430 the summer. 33:18.530 --> 33:24.030 So for the black spots, it's only for southern soil moisture in 33:24.030 --> 33:24.390 Europe. 33:24.810 --> 33:30.010 You see that when you have high soil moisture at the beginning of the 33:30.010 --> 33:35.490 summer, you have low hot days, so you inhibit heat waves. 33:35.490 --> 33:40.730 Whereas for low contents of soil moisture at the beginning of the 33:40.730 --> 33:47.210 summer, you can reach high temperature and lead to heat wave in 2003, 33:47.370 --> 33:47.870 for example. 33:48.150 --> 33:49.210 Here it's this point. 33:50.550 --> 33:57.770 That's not the case if you deal with northern moisture, because you 33:57.770 --> 33:58.750 can increase it. 33:58.750 --> 34:03.490 But it does not lead to a decrease in the percentage of hot days in 34:03.490 --> 34:04.510 continental Europe. 34:05.590 --> 34:12.350 So if you focus on southern Europe and the water management there, 34:12.790 --> 34:18.570 that can have an impact on European heat waves at the continental 34:18.570 --> 34:19.130 scale. 34:21.970 --> 34:27.050 There is also in the same line the study of Tulling et al. 34:27.110 --> 34:29.190 in Nature Geoscience 2010. 34:31.150 --> 34:37.030 At site level, so that's the pairwise sites between grassland and 34:37.030 --> 34:39.690 forest, non-distant sites. 34:40.650 --> 34:44.970 And they monitored the sensible heat flux, the latent heat flux, etc. 34:44.970 --> 34:54.110 And for the heat wave of 2003, for example, they monitored the changes 34:54.110 --> 34:57.770 in time of the sensible heat flux. 34:58.310 --> 35:05.050 And we see that at short term of the heat wave, the forest increases 35:05.050 --> 35:06.850 the temperature because it's darker. 35:08.330 --> 35:12.630 So this is why you have a sensible heat flux that is higher. 35:13.830 --> 35:16.470 Still when you have enough soil moisture. 35:17.090 --> 35:22.690 But after, at a certain threshold, you can reach dramatic increase in 35:22.690 --> 35:25.030 sensible heat flux in the case of grassland. 35:25.710 --> 35:28.870 Because of the soil moisture depletion. 35:29.530 --> 35:33.330 But forest, you can see that the sensible heat flux is more constant. 35:33.330 --> 35:41.090 So in the long term of heat wave, forest can mitigate the impact of 35:41.090 --> 35:42.870 most extreme heat waves. 35:43.790 --> 35:49.510 And we see it also there for the temperature increase in grassland 35:49.510 --> 35:51.650 areas during the heat waves. 35:51.770 --> 35:56.110 You have plus 2 degrees in the grassland. 35:56.110 --> 36:02.430 So the forest can inhibit a little the effects of the extreme heat 36:02.430 --> 36:03.490 waves in Europe. 36:05.850 --> 36:13.050 You can also think of more geoengineering stuff. 36:13.370 --> 36:17.410 Like having strong albedo crops, brighter crops. 36:17.410 --> 36:21.730 And this is what Rigel et al. 36:22.150 --> 36:26.790 in 2009 simulated with a global coupled model. 36:27.990 --> 36:33.650 And in fact, if we have brighter crops almost everywhere in the 36:33.650 --> 36:36.650 northern hemisphere, then you can reduce the top albedo. 36:37.010 --> 36:41.410 And that can lead to a 1 degree reduction in summer regionally. 36:42.410 --> 36:46.530 That's, they say, less expensive than carbon sequestration. 36:47.310 --> 36:52.510 But it implies, according to the authors, a massive genetic 36:52.510 --> 36:54.770 modification and selective breeding. 36:57.730 --> 37:02.770 And this is maybe a figure that Sebastien will describe in more 37:02.770 --> 37:05.070 details this afternoon. 37:05.070 --> 37:14.050 That's a paper of him in Nature Climate in 2014, saying that basically 37:14.050 --> 37:19.130 the biophysical effects of temperate land management, that is defined 37:19.130 --> 37:25.210 as human soil, water, vegetation treatments, use of fertilizers, 37:25.630 --> 37:27.270 species introduction, etc. 37:28.010 --> 37:32.310 is as much as important as land cover changes. 37:33.170 --> 37:37.710 So in terms of nearby temperate sites with different land management, 37:37.970 --> 37:44.230 they found that in temperate regions, you have land management changes 37:44.230 --> 37:47.450 and land cover changes that are with the same magnitude. 37:51.310 --> 37:58.530 So a small summary of other biophysical impacts of land use and land 37:58.530 --> 37:59.310 cover changes. 38:00.070 --> 38:06.350 So wind speed, we saw that it's probably reduced with afforestation 38:06.350 --> 38:07.550 and urbanization. 38:07.690 --> 38:10.010 So you have also recent study on this. 38:10.110 --> 38:12.050 It's largely unknown in general. 38:12.910 --> 38:16.310 It seems to have higher signal for temperature extreme. 38:16.310 --> 38:17.790 For example, Pittman et al. 38:19.030 --> 38:26.450 claimed that there is a significant decrease in highest percentile of 38:26.450 --> 38:32.770 Tmax, so in heat waves, due to the historical land use and land cover 38:32.770 --> 38:33.270 change. 38:34.870 --> 38:39.270 Tulling and other authors say that the forest can mitigate the mid 38:39.270 --> 38:43.330 -latitude heat waves because of more water conservations. 38:43.330 --> 38:46.590 And for the cloudiness, there was a question yesterday. 38:48.290 --> 38:53.390 The bunny fence experiment in Australia seemed to show that there is 38:53.390 --> 38:57.290 more cloudiness above forests, but that's not always the case, because 38:57.290 --> 39:03.570 you can have small clearings with high content of cloudiness. 39:04.610 --> 39:10.150 Let's think about small clearings with surrounding tropical forests, 39:10.190 --> 39:10.790 for example. 39:10.790 --> 39:15.410 You will have low pressure in surface because you have large sensible 39:15.410 --> 39:17.070 influx, high temperature. 39:17.870 --> 39:22.890 So that will favor advection from surrounding areas. 39:23.110 --> 39:24.550 We'll have like a column like that. 39:25.030 --> 39:28.710 And if you have tropical forests with a lot of soil moisture, with a 39:28.710 --> 39:33.410 lot of moisture, you have an advection of moisture around the 39:33.410 --> 39:33.990 clearings. 39:33.990 --> 39:36.590 So you will have more cloudiness. 39:36.830 --> 39:41.170 That's the cold vegetation breeze phenomenon. 39:41.430 --> 39:44.250 You have more cloudiness above clearings. 39:45.670 --> 39:51.570 So for cloudiness, it has to be investigated a little more. 39:53.330 --> 39:58.390 Bullet points for the last slides. 39:59.390 --> 40:02.450 So, biophysical impacts of deforestation. 40:02.710 --> 40:07.610 You have to take into account the three biophysical changes due to 40:07.610 --> 40:08.490 deforestation. 40:09.230 --> 40:12.430 Because forests are dark, so it implies albedo. 40:13.230 --> 40:17.610 Forests are transparent, they imply evapotranspiration efficiency. 40:18.030 --> 40:21.090 And rough, so that implies roughness net. 40:21.090 --> 40:26.890 In general, tropical deforestation leads to warmer and drier regional 40:26.890 --> 40:27.430 climate. 40:27.810 --> 40:35.190 Because the albedo, small cooling effect, is dominated by the large 40:35.190 --> 40:39.570 changes in evapotranspiration efficiency. 40:39.790 --> 40:44.870 Because tropical forests are really high LI, they are really good to 40:44.870 --> 40:45.330 evaporate. 40:46.810 --> 40:49.310 And this warming is dominating. 40:50.290 --> 40:55.250 For boreal deforestation, that leads to cooler climate in general. 40:55.390 --> 41:01.730 Because the contrast in albedo is dominating the small warming due to 41:01.730 --> 41:03.610 evaporation and roughness changes. 41:05.550 --> 41:11.050 Biophysical impacts are small globally, but they are as large as 41:11.050 --> 41:13.150 biogeochemical effects regionally. 41:13.150 --> 41:18.950 So they have to be taken into account in every afforestation project. 41:20.770 --> 41:25.110 For the net impacts of historical and future land use and land cover 41:25.110 --> 41:32.810 change, so biophysical more biogeochemical, in general, most models 41:32.810 --> 41:38.550 agree on a small global cooling effect due to the deforestation of the 41:38.550 --> 41:46.250 past century that lead to maximum 10% of decreasing warming trend. 41:47.230 --> 41:52.830 In general, no signal for the precipitation and there is still this 41:52.830 --> 41:57.010 large uncertainty concerning evapotranspiration changes due to 41:57.010 --> 41:57.750 deforestation. 41:59.010 --> 42:02.890 For future land use and land cover changes, of course, it depends on 42:02.890 --> 42:06.230 the scenario of what we will do. 42:07.090 --> 42:12.890 But globally, the net impact is not so significant for the moment 42:12.890 --> 42:16.350 among the models, both for temperature and precipitation. 42:17.110 --> 42:21.650 But the regional effects can be higher than that. 42:23.950 --> 42:24.910 So thanks a lot.