THE REGIONAL SCALE IMPACT OF LAND COVER CHANGE SIMULATED WITH A CLIMATE MODEL

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 22: (2002) Published online in Wiley InterScience ( DOI: /joc.727 THE REGIONAL SCALE IMPACT OF LAND COVER CHANGE SIMULATED WITH A CLIMATE MODEL MEI ZHAO* and A. J. PITMAN Center for Ocean-Land-Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD , U.S.A Received 19 March 2001 Revised 24 August 2001 Accepted 24 August 2001 ABSTRACT A series of 17-year integrations using the NCAR CCM3 (at about resolution) were performed to investigate the regional-scale impact of land cover change. Our aim was to determine the impact of historical land cover change on the regional-scale climate over the regions where most change occurred: Europe, India and China. The change from natural to current land cover was estimated using BIOME3 to predict the natural vegetation type, and then using remotely sensed data to estimate the locations where land cover had been changed through human activity. Results show statistically significant changes in the 15-year averaged 1000 hpa wind field, mean near-surface air temperature, maximum nearsurface air temperature and the latent heat flux over the regions where land cover change was imposed. These changes disappeared if the land cover over a particular region was omitted, indicating that our results cannot be explained by model variability. An analysis of changes on an averaged monthly time scale showed large changes in the maximum daily temperature in (Northern Hemisphere) summer and little change in the minimum daily temperature, resulting in changes in the diurnal temperature range. The change in the diurnal temperature range could be positive or negative depending on region, time of year and the precise nature of the land cover changes. Our results indicate that the inclusion of land cover change scenarios in simulations of the 20th century may lead to improved results. The impact of land cover changes on regional climates also provides support for the inclusion of land surface models that can represent future land cover changes resulting from an ecological response to natural climate variability or increasing carbon dioxide. Copyright 2002 Royal Meteorological Society. KEY WORDS: land cover change; regional climate impact; diurnal temperature range; Europe; India; China; climate modelling 1. INTRODUCTION A large number of sensitivity experiments, conducted using atmosphere models, have shown that land cover change (LCC) can influence the regional-scale climate (Pielke et al., 1998; Pitman et al., 1999). Experiments have been conducted which demonstrate that large-scale regional climate changes would result from largescale tropical deforestation (Henderson-Sellers et al., 1993; Polcher and Laval, 1994; McGuffie et al., 1995; Zhang et al., 1996a,b; Lean and Rowntree, 1997). Other studies have investigated the impact of temperate deforestation (Bonan et al., 1992) and desertification (Dirmeyer and Shukla, 1996; Xue, 1997; Nicholson et al., 1998). Claussen (1994), Foley et al., (1994), Texier et al., (1997), and Claussen et al., (1998) have also addressed the role of the land surface and suggested that the initial state of land surface has a strong control of the subsequent evolution of the climate. Humans have altered a significant fraction of the Earth s surface (Vitousek et al., 1997). Human-induced LCC is likely to accelerate in the 21st century as direct impacts via reafforestation, deforestation or agricultural intensification become supplemented by indirect effects of human activity. This indirect effect, via CO 2 fertilization or CO 2 -associated climate changes is expected to lead to global-scale changes in the * Correspondence to: Mei Zhao, Center for Ocean-Land-Atmosphere Studies, 4041 Powder Mill Road, Suite 302, Calverton, MD , U.S.A; mzhao@cola.iges.org Copyright 2002 Royal Meteorological Society

2 272 M. ZHAO AND A. J. PITMAN type, and perhaps more important, the structure, density and functional characteristics of vegetation. This paper investigates the impact that past changes may have had on the regional climate, and, by selectively including/omitting key regions of large-scale LCC, investigates whether the modelled impacts are caused by the LCCs, or by model variability. This work builds on a series of recent papers where the significance of historical LCC (from pre-industrial to present day) has been investigated (Chase et al., 2000; Zhao et al., 2001a,b) at the global scale. These showed that historical LCC has led to statistically significant changes in the climate. The aim of this paper is to determine the size of historical LCC impacts on the regional-scale climate by investigating the sensitivity of the climate to the pattern of LCC. Since these changes mainly occurred over Europe, India and China (see Figure 1) we investigate the potential contribution of LCC to the climate of these regions. In Section 2 we will briefly describe the experimental design and statistical methods. Section 3 shows the long time-scale results and Section 4 discusses the monthly and averaged diurnal time-scale results. A discussion and summary will be presented in Section METHODOLOGY We performed a series of 17-year integrations each utilizing a different LCC pattern, using the standard version of the National Center for Atmospheric Research (NCAR) CCM3 (Kiehl et al., 1996) at T42 resolution (approximately ). The NCAR CCM3 is coupled with the Biosphere-Atmosphere Transfer Scheme (BATS: Dickinson et al., 1993) and a mixed-layer ocean model. BATS is a complex land surface scheme that explicitly represents the canopy and divides the soil profile into several layers for both heat and moisture. BATS has been very extensively tested, evaluated and used in a wide range of studies. The control run used an estimate of the natural land cover (i.e. prior to human activity) obtained from BIOME3 (Haxeltine and Prentice, 1996). BIOME3 predicts the equilibrium vegetation type based on soil texture class, and monthly climate information (temperature, precipitation and humidity). We used BIOME3 at to simulate the vegetation type, which we aggregated to the resolution of the CCM3 (T42) based on the commonest vegetation type within a grid square. The current (present day) land cover data set was determined by modifying the natural land cover to the current land cover in those grid squares where leaf area index (LAI) changed by more than one according to Nemani et al. (1996), following Chase et al. (1996, 2000). We changed the land cover class in BATS in these regions from the natural land cover to (usually) crop. The change affects a large number of parameters within BATS. Table I lists parameter values and Figure 2 shows the natural and current distribution of vegetation types. More detail on creating the land cover data can be found in Zhao et al. (2001b). Overall, LCC is concentrated over Europe, India, South East Asia (mainly China), and the USA (Figure 1). Figure 1. Geographical distribution of the grid points changed between current and natural land cover

3 LAND COVER CHANGE AND CLIMATE 273 Table I. BATS parameters for each vegetation type changed in the experiments discussed in the text. BATS land cover codes are shown, where 1 = crop/mixed farming; 2 = short grass; 3 = evergreen needle leaf tree; 5 = deciduous broadleaf tree; 6 = evergreen broadleaf tree; 7 = tall grass; 10 = irrigated crop; 11 = semi-desert; 18 = mixed woodland Parameter Fractional vegetation cover Roughness length (m) Fraction of roots in top m of soil Albedo Maximum LAI Minimum LAI Stem area index Light sensitivity factor (m 2 W 1 ) Minimum stomatal resistance (s m 1 ) Depth of root zone (mm) Seasonality factor Inverse square root of leaf dimension (m 1/2 ) Displacement height (m) We have conducted five experiments that simulate the impact of different patterns of LCC: Control run used natural land cover predicted by BIOME3 globally; Experiment 1 change from natural to current land cover over all the points shown in Figure 1; Experiment 2 change from natural to current land cover over the USA, India and China (i.e. Europe was left with natural land cover); Experiment 3 change from natural to current land cover over the USA and India (i.e. Europe and China were left with natural land cover); Experiment 4 change from natural to current land cover over the USA and China (i.e. Europe and India were left with natural land cover). Most of the LCCs are confined to Eurasia. Experiments 1 4 included an identical change in land cover over the USA, since Zhao et al. (2001a,b) demonstrated that changes over the USA did not significantly affect the regional and global climates simulated with CCM3. In all experiments equilibrium was achieved after about 2 years, and results are presented as the experiment minus the control simulation. To test the statistical significance of the changes in the averages (Section 3), we use a point-by-point twotailed Student s t-test. In addition, this study aims to compare the geometric shape of the time series rather than just test mean, variance, etc. The relational grade (see Appendix A) was applied to the time series data in this paper. This statistic was first discussed by Deng (1982, 1985) for control problems of a grey system and then developed by Cao and Jiang (1993), who used it to detect a CO 2 signal in different station time series data. It is sourced from geometric similarity, essentially measuring the similarity in the geometric shape of the lines. The more similar the geometric shape of two time series data, the larger the relational grade. The relational grade is used here in preference to the correlation coefficient since it does not require a Gaussian distribution and does not require a separate statistical significance test (Cao and Jiang, 1993). More detail is provided in Appendix A.

4 274 M. ZHAO AND A. J. PITMAN Figure 2. Natural and current land cover types for each grid point changed in the experiments for Europe, China and India. BATS land cover codes are shown: 1 = crop/mixed farming; 2 = short grass; 3 = evergreen needle leaf tree; 5 = deciduous broadleaf tree; 6 = evergreen broadleaf tree; 7 = tall grass; 10 = irrigated crop; 11 = semi-desert; 18 = mixed woodland 3. RESULTS This section presents results averaged over the last 15 years of the 17-year integrations. We show changes in key fields that represent the interactions between the surface and the atmosphere: the surface wind field, mean near-surface air temperature and the latent heat fluxes Impacts on the 15-year averaged seasonal 1000 hpa wind field Figure 3 shows the 15-year seasonal average difference in the wind field between each experiment and the control run. We have masked points where the change was less than 0.8 ms 1 in order to illustrate the impact of LCC on this field clearly. Figure 3(a) (December February: DJF) and Figure 3(e) (June August:

5 LAND COVER CHANGE AND CLIMATE 275 Figure 3. The 15-year averaged seasonal difference in the 1000 hpa wind field: (a) DJF Experiment 1 (where land cover was changed over Europe, India and China) minus Control; (b) DJF Experiment 2 (where land cover was omitted over Europe) minus Control; (c) DJF Experiment 3 (where land cover was omitted over Europe and China) minus Control; (d) DJF Experiment 4 (where land cover was omitted over Europe and India) minus Control; (e) (h) as for (a) (d) respectively, but for JJA. If both the components of the wind vector were less than 0.8 m s 1 the point was set to zero

6 276 M. ZHAO AND A. J. PITMAN JJA) show the impact of replacing natural vegetation with current vegetation cover in all areas shown in Figure 1 (primarily Europe, India and China). The change in vegetation cover from forest to tall grass and crops over the three regions causes a large reduction in the roughness length, which results in increases in the 1000 hpa wind field in DJF and JJA in all three regions. Differences in the wind field can also be seen over the Indian Ocean in both seasons, over the northern Pacific in DJF and over the western Pacific in the tropics in JJA. To investigate whether these simulated changes in the wind fields are the result of the LCC or a result of model variability, we removed regions of LCC (Europe, Indian and China) one at a time. In Experiment 1 all three main regions experienced LCC. In Experiments 2 4 selected regions were replaced by natural vegetation cover (i.e. the same as the control). If this return of the land cover to natural vegetation leads to a simulated wind field over the region that matches the control experiment, then the changes seen in Figure 3(a) and 3(e) are most likely caused by the LCC perturbation rather than model variability. In Experiment 2, the LCC imposed over Europe in Experiment 1 is returned to the natural land cover used in the control integration. Figure 3(b), DJF, and Figure 3(f), JJA, show the resulting wind field (as a difference from the control). The differences seen over Europe in Figure 3(b) and 3(f) are negligible compared with those in Figure 3(a) and 3(e). No changes (greater than 1 m s 1 ) in the wind pattern occur over the location of LCC in either season, indicating that the changes seen in Experiment 1 over Europe are related directly to the LCC imposed. In Experiment 3, the LCC change over China and Europe imposed in Experiment 1 is omitted. Figure 3(c), DJF, and Figure 3(g), JJA, show no differences over either region between the control integration and Experiment 3, in contrast to the results from Experiment 1 (Figure 3(a) and 3(e)). This indicates that the changes simulated over China in Experiment 1 are the result of the imposed LCC. However, over the western Pacific, Figure 3(g) shows large changes in the JJA wind field in Experiment 3. These changes could be the result of teleconnections from the changes over India, or be due to model variability. Changes over the western Pacific occur in all the experiments, suggesting this region is one of high sensitivity in the model in JJA. In DJF in Experiment 3, the changes in the wind field, simulated over the northern Pacific in Experiments 1, 2 and 4, also disappear. The consistent pattern of changes in Experiments 1, 2 and 4, and the absence of these changes in Experiment 3 indicate that the changes in land cover over China may influence the circulation patterns in the northern Pacific Ocean. Finally, in Experiment 4, the LCC imposed over India and Europe is omitted. The changes seen in Figure 3(d), DJF, and Figure 3(h), JJA, over the Indian subcontinent are negligible compared with the changes seen in Experiment 1 (Figure 3(a) and (e)). The changes in the wind field modelled over the Indian Ocean immediately south and west of India in DJF in Experiment 1 disappear in Experiment 4, and similar reductions occur in JJA. This indicates that the changes seen over India in Experiment 1 are caused by the imposition of LCC and are not the result of model variability. Overall, therefore, as LCC over each region is individually omitted, the impact of LCC over each region, simulated in Experiment 1, disappears. This is strong evidence that the changes simulated in Experiment 1 are the result of LCC and are not caused by model variability The 15-year averaged seasonal near-surface air temperature Changes in the near-surface air temperature over the regions of LCC are clearly visible in Figure 4. In Experiment 1, statistically significant changes (calculated using a two-tailed t-test at an 80% confidence level) can be seen over India (DJF, Figure 4(a)), China (JJA, Figure 4(e)), and Europe (in particular in JJA). The changes over Europe and China in JJA exceed 1 K. When LCC over an individual region is omitted, the statistically significant changes over that region disappear. In some cases, statistically significant changes remote from the location of LCC also disappear (e.g. cooling simulated in JJA over northern Africa following LCC over Europe). These changes in the mean near-surface air temperature are strongly related to changes in the maximum near-surface air temperatures (Figure 5). Changes in the maximum temperature follow the same pattern as the changes in the mean temperature, with large statistically significant cooling over India in Experiments 1, 2

7 LAND COVER CHANGE AND CLIMATE 277 Figure 4. As for Figure 3, but for the mean near-surface air temperature (kelvin). Statistically significant differences were calculated using a point-by-point Student s t-test and shaded at a confidence level of 80% and 3 in DJF and over Europe in Experiment 1. Over China, large and statistically significant warming occurs in Experiments 1, 2 and 4. Changes in the minimum near-surface air temperature are negligible (not shown) The 15-year averaged seasonal latent heat flux The patterns of change in the simulated latent heat field (Figure 6) closely mirror the changes in the temperature fields. In Experiment 1, statistically significant changes in the latent heat field exceeding

8 278 M. ZHAO AND A. J. PITMAN Figure 5. As for Figure 4, but for the maximum near-surface air temperature (kelvin) 10 W m 2 occur over the regions of LCC in DJF (India) and JJA (China, India, Europe) (Figure 6(a) and (e)). When LCC is omitted over Europe (Figure 6(b) and 6(f)) the changes in latent heat fluxes simulated in Experiment 1 disappear over Europe. Similar results are found for China (Experiment 3, Figure 6(c) and 6(g)) and India (Experiment 4, Figure 6(d) and 6(h)). These changes in latent heat cannot be explained by changes in precipitation, which were negligible.

9 LAND COVER CHANGE AND CLIMATE 279 Figure 6. As for Figure 4, but for the latent heat flux (W m 2 ) 4. IMPACTS OF LCC ON THE MONTHLY AVERAGE QUANTITIES The changes in the large-scale wind patterns, temperatures and latent heat fluxes discussed in Section 3 provide evidence that regional-scale LCC affects the regional-scale climate. In this section, we focus on the impact of LCC on the regional-scale climate in more detail by investigating changes on a seasonal time scale.

10 280 M. ZHAO AND A. J. PITMAN 4.1. Europe Experiments 2 4 used natural land cover over Europe; hence, we would expect to see differences between these integrations and Experiment 1, where land cover was modified over Europe (see Figure 2). Figure 7(a) shows the changes in temperature simulated over the locations where land cover was changed over Europe. It is clear that the mean near-surface air temperature change in the simulations where LCC was imposed over Europe is larger and seasonally different from the other experiments. In JJA, the temperature is cooler by almost 1 K compared with the other experiments. This difference is statistically significant at an 80% confidence level (calculated using a point-by-point two-tailed Student s t-test; see Figure 4(a) and 4(e)). Table II shows that the relational grade (Appendix A) in the monthly variation in mean near-surface air temperature between Experiment 1 and Experiments 2 4 is very low (less than 0.24), whereas the relational grade between Experiments 2 4 is much higher (more than 0.7). This indicates that results from Experiment 1 are statistically different from Experiments 2 4 and these latter three experiments produce very similar results. This demonstrates that model variability is not significant in explaining our results. The reduction in the mean temperature in JJA ( 1K) is caused by a significant reduction in the maximum daily temperature (Figure 7(b)), which is reduced by up to 1.5 K. Table II shows that Experiment 1 has a low relational grade with Experiments 2 4 but, as with the mean temperature, Experiments 2 4 are similar to each other. The minimum temperature (Figure 7(c)) is not clearly different between the experiments at any time of the year, although Table II indicates a much higher level of correlation between Experiments 2 4 than between Experiment 1 and Experiments 2 4. Overall, as a net result of the changes in maximum and minimum temperature, the change in the magnitude of the diurnal range (Figure 7(d)) is more than 1 K in the experiment with LCC over Europe in JJA (Experiment 1) and negligible in those experiments where land cover was not changed (Experiments 2 4). The reduction in the magnitude of the diurnal temperature range is closely associated with a change in the partitioning of available energy between sensible (Figure 7(e)) and latent heat fluxes (Figure 7(f)). The sensible heat flux in May through to August is reduced and the latent heat flux increased. In June and July, the increase in the latent heat flux exceeds 15 W m 2. These changes are not associated with changes in net radiation or precipitation (not shown). The change in land cover in Europe is largely from forests to crops. In BATS, this leads to a variety of parameter changes (see Tables I and III), including an increase in fractional vegetation cover, a reduction in roughness length, a change in the root distribution, a large reduction in canopy resistance, a large reduction in the depth of the root zone and changes in the LAI. The increase in the fraction of roots in the deeper layers, coupled with the reduced stomatal resistance and the increase in maximum LAI in summer combine to increase the latent heat flux (Figure 7(f)) and thereby reduce the sensible heat flux (Figure 7(e)); (note that the reduction in the depth of the root zone is only significant if soil moisture is limiting, which is not the case in these experiments over Europe). The increase in the latent heat causes additional cooling during the day, reducing the daily maximum temperature. Cooling during the night is not affected, as it is largely driven by radiative heat loss. Overall, therefore, the magnitude of the diurnal temperature range is reduced India Experiments 1 3 included LCC over India; hence we would expect the main differences to be seen in Experiment 4. Over India, LCC causes cooling in the mean daily near-surface air temperature (Figure 8(a)). The cooling varies between 0.25 and 0.75 K in January, reducing to less than 0.25 K in July. As with Europe, the change in the mean temperature is largely explained by the reduction of 1.5 K in the maximum daily temperature (Figure 8(b)). In Experiment 4 (where LCC was omitted over India) the maximum temperature is warmer from August to March (by less than 0.5 K). However, this change is greatly exceeded by the cooling caused by LCC in Experiments 1 3. The changes in the minimum daily temperature are small (Figure 8(c)). The net change (Figure 8(d)) is therefore a large reduction in the diurnal range in the experiments with LCC over India, in particular between July and December. The changes in temperature are again caused by changes in the partitioning of available energy. The sensible heat is reduced (Figure 8(e)) by up to about 5 W m 2 and latent heat is increased by a similar amount (Figure 8(f)).

11 LAND COVER CHANGE AND CLIMATE 281 Figure 7. Monthly average (over 15 years) differences (from the control) in (a) mean, (b) maximum, (c) minimum, (d) diurnal change in near-surface air temperatures, (e) sensible heat flux, and (f) latent heat flux averaged over the points where land cover was changed over Europe (see Figure 2). Differences between Experiment 1 and the Control are shown by the line with circles; differences between Experiment 2 and the Control are represented by squares; differences between Experiment 3 and the Control are represented by triangles; differences between Experiment 4 and the Control are represented by crosses The changes in latent and sensible heat fluxes are caused by changes in the BATS parameter values (see Tables I and III). In particular, the distribution of more roots deeper into the soil and reductions in the canopy resistance explain the increase in the latent heat flux, and thereby the cooler maximum temperatures. The relational grade between the experiments with LCC over India and those without (Table IV) shows a very low relational grade between Experiment 4 and Experiment 1 3, but a very high relational grade between Experiments 1 3. This indicates that the monthly variation in the mean, maximum and minimum temperatures is strongly affected by LCC China Land cover was changed over China in Experiments 1, 2 and 4; hence we would expect to see differences between these experiments and Experiment 3, where land cover was not changed from the control. The LCC

12 282 M. ZHAO AND A. J. PITMAN Table II. The relational grade (see Appendix A) between the experiments of 15 year area-averaged monthly time series over Europe. The italicized row identifies the experiment where land cover was different Experiment 2 Control Experiment 3 Control Experiment 4 Control Mean temperature Experiment 1 Control Experiment 2 Control Experiment 3 Control 0.85 Maximum temperature Experiment 1 Control Experiment 2 Control Experiment 3 Control Minimum temperature Experiment 1 Control Experiment 2 Control Experiment 3 Control Max min temperature Experiment 1 Control Experiment 2 Control Experiment 3 Control Table III. The average regional change in BATS parameters effected by the prescribed LCC. The changes are calculated over the points where LCC was imposed (see Figure 2) Parameter Europe India China North China South China Fractional vegetation cover Roughness length (m) Fraction of roots in top 0.1 m of soil Albedo Maximum LAI Minimum LAI Stem area index Light sensitivity factor (m 2 W 1 ) Minimum stomatal resistance (s m 1 ) Depth of root zone (mm) Seasonality factor Inverse square root of leaf dimension (m 1/2 ) Displacement height (m) changes over China lead to significant temperature increases, in contrast to the LCC over India and Europe. The warming in the mean daily temperature is about 0.5 K from June to October (Figure 9(a)), but reaches 0.8 K in July in Experiment 1. The changes in temperature over China in Experiment 3 (where LCC was omitted over China) are negligible between May and September. The change in the mean daily temperature is caused by a large increase in the maximum daily temperature (Figure 9(b)), by more than 1 K between June and October and by more than 1.5 K between July and October in Experiments 1 and 2. Negligible changes in the minimum daily temperature occur (Figure 9(c)) and hence the diurnal range increases significantly (Figure 9(d)). There is a low relational grade between Experiment 3 and Experiments 1, 2 and 4, but a high relational grade between Experiments 1, 2 and 4 (Table V). This indicates that the monthly variation in each of the mean, maximum and minimum temperatures is strongly affected by LCC.

13 LAND COVER CHANGE AND CLIMATE 283 Figure 8. As Figure 7, but for points where land cover was changed over India (see Figure 2) The changes in the temperature are associated with an increase in sensible heat flux (Figure 9(e)) and a reduction in the latent heat flux (Figure 9(f)). These changes are caused by large reductions in LAI and other parameters (Table III). The change in land cover over China is geographically distinct. In the northern part of the region the change in land cover is mainly from deciduous broadleaf forest and mixed woodland to crop mixed farming (Figure 2). In the southern part of the region the change is mainly from evergreen broadleaf forest to a mixture of crop mixed farming, mixed woodland and short grass (Figure 2). In effect, the LCC in southern China is analogous to a traditional tropical deforestation experiment (see Henderson-Sellers et al., 1993; Polcher and Laval, 1994; McGuffie et al., 1995; Zhang et al., 1996a,b; Lean and Rowntree, 1997). Over the northern part of the region (north of 31 N) the parameter changes include a reduction in roughness length (by 0.72 m), a reduction of minimum LAI (by about 1.25), a reduction in the stem area index ( 1.21) and a significant reduction of 73 s m 1 in the minimum stomatal resistance (see Table III). In contrast, the parameter changes in the southern part of the region include a larger reduction in roughness length (by 1.45 m), a larger reduction in minimum LAI ( 2.91), an increase in stem area index (0.23) and a small increase in the minimum stomatal resistance ( 7 sm 1 ) (Table III). The differences in the parameter changes between northern and southern China are reflected in the simulated surface energy balance. In northern China, the change in mean near-surface air temperature is

14 284 M. ZHAO AND A. J. PITMAN Table IV. The relational grade (see Appendix A) between the experiments of 15 year area-averaged monthly time series over India. The italicized row identifies the experiment where land cover was different Experiment 1 Control Experiment 2 Control Experiment 3 Control Mean temperature Experiment 4 Control Experiment 1 Control Experiment 2 Control Maximum temperature Experiment 4 Control Experiment 1 Control Experiment 2 Control Minimum temperature Experiment 4 Control Experiment 1 Control Experiment 2 Control Max min temperature Experiment 4 Control Experiment 1 Control Experiment 2 Control small (Figure 10(a)), and changes in the maximum temperature reaches about 0.5 K following LCC. It is also difficult to differentiate clearly between the results of the four experiments, in that the changes induced by LCC are not clearly different from Experiment 3, where the land cover was not changed. In contrast, in southern China, the impact of LCC (Figure 10(a)) includes a larger increase in the mean near-surface air temperature ( K), and results from the experiments with LCC are distinct from Experiment 3, which omitted LCC. The maximum temperature increases by more than 1.5 K during most of the year (Figure 11(b)), whereas in Experiment 3 the change in the maximum temperature is negligible. Overall then, in northern China, the change in the diurnal range is K and marginally distinct from Experiment 3, whereas in southern China the change is up to 2.5 K and strongly distinct from Experiment 3. The impact of LCC on the surface energy balance in northern and southern China is also geographically distinct. In northern China, the change in sensible (Figure 10(e)) and latent heat (Figure 10(f)) is negligible. In contrast, in southern China, the changes in these fluxes are large and latent heat is reduced by W m 2 in June to September (Figure 11(e) and 11(f)). The relatively small impact of LCC over northern China contrasts dramatically with the large impact of LCC over southern China. In effect, the geographical extent of LCC in southern China is of the order of half the geographicalextent of a traditionaltropical deforestation experiment, and, given that the parameterchangesare similar, a large-scale impact should be expected. The changes in the characteristics of the vegetation cover over southern China (in particular the minimum stomatal resistance, LAI, reduction in vegetation cover, increase in albedo and large reduction in roughness length; Table III) combine to reduce evaporation. The reduced roughness length decreases the turbulent energy fluxes, which tends to increase temperature (Sellers, 1992; Zhang et al., 1996a,b). The increase in albedo reduces the energy available for the turbulent energy fluxes, which tends to increase the temperature. Zhang et al. (1996a,b) also point to a significant reduction in evaporation resulting from reductions in LAI and stem area index. 5. DISCUSSION AND SUMMARY A consistent pattern of large and statistically significant changes over the location of LCC in the 15-year seasonal averages (Section 3) was identified. In particular, the consistent signal of negligible changes over a region unless land cover was modified is evidence that the changes that occur when land cover is modified

15 LAND COVER CHANGE AND CLIMATE 285 Figure 9. As Figure 7, but for points where land cover was changed over China (see Figure 2) are not related to model variability. The large-scale regional cooling over Europe, northern China and India, resulting from the LCC, and the warming over southern China were the result of large changes in the maximum temperatures and were coincident with changes in the latent heat flux. The changes in the latent heat flux were caused by changes in parameter values (changes in precipitation were negligible). The changes in the diurnal temperature range were an unexpected result from these experiments. This occurs due to changes in surface energy balance caused by LCC that affect the diurnal temperature in different ways at different times during the day. At night, the lack of turbulent energy fluxes means that the surface energy balance is dominated by radiative exchange, which is not significantly affected by LCCs. In contrast, around noon, when the maximum temperature is reached, changes in land cover that affect evaporation can have a large impact on the maximum temperature. Over Europe and India, the increase in vegetation cover, reductions in minimum stomatal resistance, and the increase in the fraction of roots in the deeper soil layers combine to increase evaporation during the day. Higher evaporation around noon suppresses temperature, reducing the maximum, and therefore reducing the diurnal range. In contrast, in China, the parameter changes restrict evaporation during the day, reducing noon-time cooling and allowing higher temperatures to be simulated, which increases the magnitude of the diurnal range.

16 286 M. ZHAO AND A. J. PITMAN Table V. The relational grade (see Appendix A) between the experiments of 15 year area-averaged monthly time series over China. The italicized row identifies the experiment where land cover was different Experiment 1 Control Experiment 2 Control Experiment 4 Control Mean temperature Experiment 3 Control Experiment 1 Control Experiment 2 Control Maximum temperature Experiment 3 Control Experiment 1 Control Experiment 2 Control 0.99 Minimum temperature Experiment 3 Control Experiment 1 Control Experiment 2 Control Max min temperature Experiment 3 Control Experiment 1 Control Experiment 2 Control 0.96 The large-scale nature of LCCs induced through human activity, and the indication that these changes have the potential to influence the regional-scale climates of those regions affected, suggests that LCC scenarios should be included in attempts to simulate the climate of the 20th century. The impact of increased greenhouse gases is expected to reduce the magnitude of the diurnal temperature range by increasing night-time temperatures. LCC appears to be able to affect the diurnal temperature range, increasing or decreasing the maximum temperature, depending on the nature of the actual change in vegetation parameter values. Since the impact of increasing greenhouse gases is to decrease the diurnal range, and the impact of LCC can be to change the diurnal temperature range in either direction, the interpretation of any observed changes in this quantity will have to be attributed to a cause with care. Our results also suggest that future changes in land cover need to be taken into account. Future changes in land cover can be caused by further human activity, by ecological responses to natural climate variability and by ecological responses to human-induced climate changes, the most significant of which is likely to be increases in CO 2 in the atmosphere. These future changes may be very significant in the way they influence the supply of water for evaporation. Recent work improving land surface schemes via linking interactive biosphere models with climate models provides a means to include indirect changes in land cover caused by increases in CO 2 (Foley et al., 1998). The direct effects of LCC, via deforestation, reafforestation or agricultural intensification, will need to be included as scenarios in future experiments. Our results indicate, at least at the regional scale in areas of intensive human modification, that the inclusion of these models, which may be able to represent changes in the ecology, is a priority, especially if impacts of climate change on surface quantities are considered important. APPENDIX A: STATISTICAL METHODOLOGY The relational grade was used to test the statistically significant difference between the time-series data. This measures the similarity of geometric shape between the lines. The more similar the geometric shape between the time-series data, the larger the relational grade.

17 LAND COVER CHANGE AND CLIMATE 287 Figure 10. As Figure 7, but for points where land cover was changed over northern China (see Figure 2, points north of 31 N) Choose a time series of length n where y ={y 1,y 2,...,y n } as the reference variable. There are m time-series data as the detected variables (x i ={x i1,x i2,...,x in },i = 1, 2,...,m). The relational grade will calculate the level of similarity between the reference variable and each detected variable. First, apply the standardization range analysis to both the reference and detected original time-series data, i.e. y j = y j min(y) max(y) min(y) x ij = x ij min(x) i max(x) i min(x) i j = 1, 2,...,n (1) i = 1, 2,...,m j = 1, 2,...,n (2) where max(y) and min(y) are the maximum and minimum values in the reference time-series data and max(x) i and min(x) i are the maximum and minimum values in each detected time-series data. In order to

18 288 M. ZHAO AND A. J. PITMAN Figure 11. As Figure 7, but for points where land cover was changed over southern China (see Figure 2, points south of 31 N) simplify the explanation, we will use y j or x ij in the following text rather than y j or x ij. We now have: y 1 x 11 x 21 x x m1 y 2 x 12 x 22 x x m y n x 1n x 2n x 3n... x mn (3) Then, for each row, sort the detected time series (x ij ) in descending order according to the distance between y j and x ij : S = y j x ij (4)

19 LAND COVER CHANGE AND CLIMATE 289 and Equation (3) may become y 1 x 21 x 31 x m1... x 11 y 2 x 22 x m2 x x y n x 3n x 1n x 2n... x mn (5) Then count how many times each i = 1, 2,...,m appears in the first (maximum distance, l i ) and last (minimum distance, s i ) columns. Third, calculate the coefficient of relational grade between the reference and detected variables as: ξ ij = 1 i = 1, 2,...,m 1 + a i (y j x ij ) 2 j = 1, 2,...,n where a i is a weight coefficient that measures the distance and relation between the ith detected variable and reference variable; it is given as: where a i = u i /v i (7) u i = 1 l i + 1 v i = 1 s i + 1 l i l=1 s i max x ijl (8) min x ijs (9) s=1 l i and s i are the frequencies counted from Equation (5). When i = 1,x 1j may appear in the first column when j = 4, 8, and 12 (j l ), or in the last column j = 1, 5, and 11 (j s ),etc.hence,whenj = j l,orj = j s : max x ijl = max(y jl x ijl ) 2 min x ijs = min(y js x ijs ) 2 (10) The coefficient of relational grade ξ ij gives the correlation between the reference variable and detected variables at each time point (j = 1, 2,...,n). The relational grade is the combined index of those correlations for the whole time-series data. Finally, average the coefficient of relational grade as the relational grade: r i = 1 n n ξ ij i = 1, 2,...,m (11) j=1 Larger values of r i indicate higher similarity between reference time series and the ith detected time series. In creating Tables II, IV and V we selected one time series as the dependent series (i.e. Experiment 1 in Table II, Experiment 4 in Table IV and Experiment 3 in Table V) and used the other experiments as the independent time series. (6) REFERENCES Bonan GB, Pollard D, Thompson SL Effects of boreal forest vegetation on global climate. Nature 359: Cao HX, Jiang Y The relation analysis of increasing CO 2 and temperature change. In Climatology Research: Climate and Climate in China, Yao ZS (ed.). Meteorological Press: Beijing; (in Chinese). Chase TN, Pielke RA, Kittel TGF, Nemani RR, Running SW Sensitivity of a general circulation model to global changes in leaf area index. Journal of Geophysical Research 101:

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