Sensitivity of Climate Changes to CO2 Emissions in China

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Sep 16, 2014 - sensitivity is high (2.4%/°C) for heavy precipitation days. (> 10 mm d−1) and ... This rate of CO2 increase is comparable to that under the IPCC's RCP8.5 (Repre- ... simple arithmetic average from all models employed in this study. .... parts of northwestern China, with a rate of greater than. 5.0%/°C except in ...

ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2014, VOL. 7, NO. 5, 422427

Sensitivity of Climate Changes to CO2 Emissions in China CHEN Huo-Po1,2 and SUN Jian-Qi1 1 2

Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received 11 March 2014; revised 14 April 2014; accepted 14 April 2014; published 16 September 2014

Abstract In this study, the authors demonstrate that the Coupled Model Intercomparison Project Phase 5 (CMIP5) models project a robust response in changes of mean and climate extremes to warming in China. Under a scenario of a 1% CO2 increase per year, surface temperature in China is projected to increase more rapidly than the global average, and the model ensemble projects more precipitation (2.2%/°C). Responses in changes of climate extremes are generally much stronger than that of climate means. The majority of models project a consistent response, with more warm events but fewer cold events in China due to CO2 warming. For example, the ensemble mean indicates a high positive sensitivity for increasing summer days (12.4%/°C) and tropical nights (26.0%/°C), but a negative sensitivity for decreasing frost days (−4.7%/°C) and ice days (−7.0%/°C). Further analyses indicate that precipitation in China is likely to become more extreme, featuring a high positive sensitivity. The sensitivity is high (2.4%/°C) for heavy precipitation days (> 10 mm d−1) and increases dramatically (5.3%/°C) for very heavy precipitation days (> 20 mm d−1), as well as for precipitation amounts on very wet days (10.8%/°C) and extremely wet days (22.0%/°C). Thus, it is concluded that the more extreme precipitation events generally show higher sensitivity to CO2 warming. Additionally, southern China is projected to experience an increased risk of drought and flood occurrence, while an increased risk of flood but a decreased risk of drought is likely in other regions of China.  Keywords: sensitivity, climate extreme, CO2 warming, China, CMIP5 Citation: Chen, H.-P., and J.-Q. Sun, 2014: Sensitivity of climate changes to CO2 emissions in China, Atmos. Oceanic Sci. Lett., 7, 422–427, doi:10.3878/j.issn.16742834.14.0028.

1

Introduction

Climate extreme events and their changes are of particular relevance to society and ecosystems due to their potentially severe impacts. The Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) has documented that some extremes have changed as a result of anthropogenic influences, including in

Corresponding author: CHEN Huo-Po, [email protected]

creases in atmospheric concentrations of greenhouse gases (GHG) (IPCC, 2013). This finding is also emphasized by its Special Report on Extreme Events (SREX), which details ‘likely confidence’ that anthropogenic influences have led to warming of extreme daily minimum and maximum temperatures, and ‘medium confidence’ that anthropogenic influences have contributed to intensification of extreme precipitation at the global scale (IPCC, 2012). There is thus a high level of confidence that the role of human activity in recent climate changes, as well as in future climate changes, cannot be ignored (Gao et al., 2012; Wang et al., 2012; Chen et al., 2014; Oppenheimer, 2013). However, few studies have been published so far that assess the sensitivity of climate changes to human activity, although more and more studies suggest that there is an increased risk of climate extreme events under a warmer scenario, including more intense extreme precipitation events, snowfall, drought and flood events, and so on (Xu et al., 2009, 2011, 2012a, b; Sun et al., 2010; Li et al., 2011; Zhang and Sun, 2012; Chen, 2013; Chen et al., 2013). Recently, a study by Lau et al. (2013) indicated that the increasing CO2 emissions will induce more heavy precipitation, less moderate precipitation, more light precipitation, and longer dry periods, from the global perspective. But what about the potential changes to extreme climate in response to CO2 warming at the regional scale? This area is considered to be a much more complex and challenging issue. In this context, the main aim of this study is to perform a preliminary analysis of the sensitivity of climate extreme change to increased CO2 emissions in China on the basis of the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations. Compared to CMIP3, CMIP5 includes a wider variety of experiments and applies more comprehensive models to address a greater variety of scientific questions (Taylor et al., 2012). To address the above issue, the CMIP5 simulations on the basis of a 140-yr experiment with a prescribed 1% CO2 increase per year are employed in this study. The remainder of the paper is structured as follows. Section 2 provides a brief description of the model dataset and methods; section 3 presents the results of the sensitivity of climate extreme changes to increased CO2 emissions in China; and section 4 presents some concluding remarks.

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CHEN AND SUN: SENSITIVITY OF CLIMATE CHANGES IN CHINA

Data and method

The main aim of this study is to assess CMIP5 model projections of the regional climate response to GHG emissions; specifically, to increased CO2 emissions. Thus, the outputs of 20 CMIP5 models based on a 140-yr experiment with a prescribed 1% per year increase in CO2 emission are used in this study. This rate of CO2 increase is comparable to that under the IPCC’s RCP8.5 (Representative Concentration Pathway) scenario, but the latter also includes changes in other GHGs and aerosols (Riahi et al., 2011). Different to previous studies, which have mostly focused on the climate mean response, we not only emphasize these changes, but also those of extreme events, including warm and cold events, extreme precipitation, drought, and flood events. Thus, monthly near-surface temperature and monthly precipitation accumulation, as well as daily minimum and maximum near-surface temperatures and daily precipitation rates, are retrieved from the Earth System Grid (ESG) data portal for 20 CMIP5

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models for a scenario of a 1% CO2 increase per year. On the basis of simulated daily datasets, a popular set of 27 climate extreme indices (Table 1), defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), are calculated. These indices in general describe moderate extreme events with a reoccurrence time of one year or less, and more detailed information can be found in previous studies (e.g., Zhang et al., 2011). The 20 CMIP5 models used in this study have diverse resolutions. For convenience of analysis, the monthly near-surface temperature and precipitation and the calculated 27 extreme indices are regridded to a common 2.5° × 2.5° (longitude × latitude) grid using a first-order conservative remapping procedure. Then, the multi-model ensemble (MME) method is used, which is defined as the simple arithmetic average from all models employed in this study. The climate sensitivity (dP/P/dT) to global warming is defined as the difference in the statistics between the control and the periods that correspond approximately to a doubling of CO2 emissions (DCO2) and a

Table 1 Sensitivity (dP/P/dT) of multi-model ensemble (MME) climate extreme indices to warming due to CO2 emissions in China. Uncertainties are estimated from one inter-model standard deviation for climate extreme indices. Units: %/°C. DCO2

TCO2

Max TX

3.5 ± 0.6

3.5 ± 0.5

TXn

Min TX

6.8 ± 1.2

6.9 ± 1.1

TNx

Max TN

5.6 ± 0.5

5.5 ± 0.5

TNn

Min TN

4.7 ± 0.9

4.8 ± 0.8

FD

Frost days

−4.8 ± 0.7

−4.7 ± 0.6

ID

Ice days

−7.1 ± 1.0

−7.0 ± 0.8

SU

Summer days

12.5 ± 3.3

12.3 ± 3.2

TR

Tropical nights

25.2 ± 7.7

27.1 ± 8.9

Label

Index name

TXx

GSL

Growing season length

2.1 ± 0.6

2.2 ± 0.4

DTR

Diurnal temperature range

−0.5 ± 0.8

−0.5 ± 0.6

TN10p

Cold nights

−28.2 ± 2.4

−19.8 ± 2.5

TX10p

Cold days

−24.6 ± 1.8

−18.3 ± 2.1

TN90p

Warm nights

100. 7 ± 17.9

107.2 ± 17.4

TX90p

Warm days

80.1 ± 11.6

86.7 ± 10.8

WSDI

Warm spell duration

394.6 ± 129.3

574.4 ± 194.5

CSDI

Cold spell duration

−31.4 ± 3.5

−20.5 ± 2.9

RX1day

Max one-day precipitation

5.0 ± 1.4

5.1 ± 1.3

RX5day

Max five-day precipitation

4.0 ± 1.5

4.0 ± 1.4

SDII

Simple daily precipitation

2.7 ± 0.5

2.6 ± 0.5

R1mm

Number of wet days

0.2 ± 1.4

0.1 ± 1.2

R10mm

Heavy precipitation days

2.5 ± 1.5

2.3 ± 1.3

R20mm

Very heavy precipitation days

5.4 ± 2.5

5.1 ± 1.7

CDD

Consecutive dry days

−1.6 ± 2.0

−1.2 ± 1.9

CWD

Consecutive wet days

−0.5 ± 2.0

−0.5 ± 1.6

R95p

Very wet days

10.9 ± 3.6

10.8 ± 2.8

R99p

Extremely wet days

21.9 ± 6.2

22.8 ± 6.1

PRCPTOT

Total wet-day precipitation

2.4 ± 1.5

2.3 ± 1.2

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ATMOSPHERIC AND OCEANIC SCIENCE LETTERS

mosphere-Ocean Coupled Climate Model version 3 of Meteorological Research Institute (MRI-CGCM3) model. In the case of a 1% CO2 increase per year, annual precipitation increases by 6.1% under DCO2 and 9.9% under TCO2, with a sensitivity of 2.2%/°C, substantially larger than the 1.4%/°C for global annual precipitation (Lau et al., 2013). Different performances are clear for different seasons. For instance, the precipitation change in spring shows the strongest response to warming due to the gradually increased CO2 emissions, with a sensitivity of 3.0%/°C, followed by winter (2.8%/°C), and finally summer (1.5%/°C). The response of annual precipitation demonstrates a high spatial variability, as well as in seasons (Figures not shown). Much stronger sensitivity is dominant over most parts of northwestern China, with a rate of greater than 5.0%/°C except in the southwest Xinjiang region. The climate changes in Northeast and North China show relatively weaker sensitivity to warming, with a rate of 2.0–3.0%/°C. The sensitivity in southern China is the weakest, with most parts dominated by a rate of less than 1.0%/°C. It is important to note at this point that the sensitivities under DCO2 and TCO2 are observed to be approximately constant in terms of the changes of annual mean precipitation over China, indicating a quasi-steady state having been reached for the response of precipitation change.

tripling of CO2 emissions (TCO2) compared to the control. The control statistics are assessed by the first 20-yr period of the integration, and the CO2 emission would be nearly doubled in year 70 and tripled by year 110. Thus, the regional climate responses to DCO2 and TCO2 are defined as the difference in climate statistics from the averages in the mid 20 years (years 70–89) and the last 20 years (years 110–129) to the control, respectively. Since the responses based on DCO2 and TCO2 are similar, except with a stronger and more robust signal in the latter, unless otherwise stated, results presented are for DCO2.

3 3. 1

Results Response of climate means

All models present a clear increase in global mean temperature due to increased CO2 emissions, with a linear trend of 0.22–0.41°C per decade, and the MME of 0.32°C per decade. The warming trends in China are much larger than global means for all models (Fig. 1a), with a rate of 0.31–0.58°C per decade among models, and the MME of 0.44°C per decade. Compared to the control, warming is much stronger in winter, but weaker in spring for DCO2 in China, with the surface temperature increasing by 2.99 ± 0.52°C and 2.48 ± 0.48°C, respectively. Increases in summer and autumn are more comparable to the annual means (2.72 ± 0.44°C for DCO2 and 4.66 ± 0.44°C for TCO2). Similarly, all models show a clear upward trend in annual mean precipitation for China, with an ensemble mean rate of 0.02 mm d−1 per decade or 0.91% per decade (Fig. 1b). However, a much larger model spread can be observed in the changes of precipitation than temperature, with the lowest rate of increase being 0.21% per decade in the Earth System Model of the Institut Pierre Simon Laplace: Medium Resolution (IPSL-CM5A-MR) model and the highest being 1.83% per decade in the Global At-

3.2

0.6 annual precipitation change [ mm/day ]

annual temperature change [ oC ]

5 4 3 2 1 0

Response of climate extremes

The change characteristics of climate extremes are analyzed in this subsection. Generally, the warm extremes are more frequent and warmer, while the cold extremes are less frequent and weaker due to CO2 warming when compared to the control. Meanwhile, an increase tendency is continually apparent for the precipitation-related extremes over China, except consecutive dry days (CDD) and consecutive wet days (CWD).

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(b)

0.5 0.4 0.3 0.2 0.1 0 −0.1

−1 20

40

60 80 time [ year ]

100

120

140

−0.2

20

40

60 80 time [ year ]

100

120

140

Figure 1 Projected changes in annual (a) surface temperature and (b) precipitation in China induced by increased CO2 emissions from year 1 to 140, on the basis of the experiment with a 1% increase in CO2 emissions per year. The curves represent the MME changes from 20 CMIP5 models and shading indicates the uncertainty by one standard deviation among models.

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For the warm extremes, the hottest day of the year (TXx), calculated from the maximum of daily maximum near-surface temperature, is projected to increase significantly due to CO2 warming, with a rate of 0.26–0.59°C per decade among models, and the MME of 0.46°C per decade. Meanwhile, the coldest day of the year (TNn), estimated from the minimum of daily minimum nearsurface temperature, is also projected to increase significantly, with a rate of 0.40–0.69°C per decade among models, and the MME of 0.53°C per decade. Due to these changes, the response of TNn (4.7%/°C) to CO2 warming is much stronger than that of TXx (3.5%/°C) under both DCO2 and TCO2 scenarios (Table 1). Thus, most models present a weak decrease in diurnal temperature range (DTR), with an MME sensitivity of −0.5%/°C. However, it should be noted that high inter-model variability can be observed, and the spread is even larger than the change magnitude of the MME (Table 1). The sensitivity of DTR also shows an obvious regional difference, with a positive

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sensitivity for increasing DTR in southern China and a negative sensitivity for decreasing DTR in the other regions of China (Fig. 2a). Generally, the warm events, including summer days (SU) and tropical nights (TR), present a positive sensitivity, while the cold events, such as frost days (FD) and ice days (ID), show a negative sensitivity to the CO2 emissions (Table 1). Summer days, based on a fixed 25°C threshold, increase significantly by 57.3%, with a rate of sensitivity of 12.3%/°C under the TCO2 scenario. Tropical nights, based on a fixed 20°C threshold, double under TCO2, with a sensitivity of 27.1%/°C. However, a negative tendency is clear for the changes of FD and ID, with a rate of −3.8 days per decade and −3.0 days per decade, respectively. Furthermore, a relative weaker response can be observed in these cold events when compared to the warm events, with a sensitivity of −4.7%/°C for FD and −7.0%/°C for ID. High variability is also clear in terms of its geographic distribution. Taking FD and SU as exam-

Figure 2 Geographic distribution of the MME mean response in climate extreme indices to increased CO2 induced warming in China for DCO2. The panels in the left column show the sensitivities of temperature-related extremes for (a) DTR, (b) FD, and (c) SU; and the panels in the right column show the sensitivities of precipitation-related extremes for (d) R20mm, (e) CDD, and (f) RX5day. Units: %/°C.

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ATMOSPHERIC AND OCEANIC SCIENCE LETTERS

ples (Figs. 2b and 2c), stronger negative sensitivity for decreasing FD is dominant over southern China, and relatively weaker in other regions; while a positive sensitivity for increasing SU covers the whole of China, with stronger sensitivity in Tibet, Northeast China, and some parts of North China, and weaker sensitivity in southern China and the arid regions of Northwest China. In response to warming due to CO2 emissions, the growing season length (GSL) also increases significantly in China, with a tendency of 2.1 days per decade or 0.97% per decade. Considering China as a whole, the GSL is projected to increase by 12.5 days under DCO2 and 22.8 days under TCO2 compared to the control, but with a similar sensitivity of 2.2%/°C under these two scenarios. The relative changes in percentile indices, including cold nights (TN10p), cold days (TX10p), warm nights (TN90p), warm days (TX90p), warm spell duration (WSDI), and cold spell duration (CSDI), are generally much larger than those in absolute indices, as well as the sensitivity to CO2 warming (Table 1). This may be associated with the definitions of percentile indices, which are exceedance rates (%) above or below the calculated thresholds. However, similar tendencies of warm and cold events can be observed between these percentile and absolute indices. Among the temperature-related extremes, WSDI shows the highest sensitivity to CO2 warming, and is projected to lengthen four- or even five-fold when the surface temperature increases by 1°C, but with high inter-model variability. An analysis of changes in precipitation-related extremes indicates that there is likely to be more rainfall days (R1mm) in the future. However, a large model spread is clear and only 11 models show an increase tendency, as well as its sensitivity to CO2 warming. Meanwhile, all models project a clear increase in simple daily precipitation intensity (SDII), and the model ensemble mean indicates that SDII will increase by 2.6% if the surface temperature increases by 1°C in China. Thus, we can conclude that precipitation events in China are likely to be more extreme in the future due to CO2 emissions. This conclusion also holds true from analyzing the other extreme indices. All models show an increase tendency of changes in heavy precipitation days (R10mm) and very heavy precipitation days (R20mm) in China, with the MME rate of 1.0% per decade and 2.2% per decade, respectively. For R10mm and R20mm, a positive sensitivity covers the whole of China, but the sensitivity is quite high in the west and low in the east (Fig. 2d). In the case of DCO2, the sensitivity is positive for both R10mm and R20mm (0.36.6%/°C and 1.413.2%/°C, respectively). Similar magnitudes can be observed under the TCO2 scenario, and the MME results show a positive sensitivity of 2.3%/°C for R10mm and 5.1%/°C for R20mm. Furthermore, the CO2 warming also induces significant increases of total precipitation amounts on very wet days (R95p) and extremely wet days (R99p), and they show a relatively stronger response to CO2 emissions. The ensemble-mean sensitivity is quite high (10.9%/°C) for R95p

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and increases dramatically (21.9%/°C) for R99p. From the above analysis, the key point is that, in general, more extreme precipitation events show a much higher sensitivity to increased CO2 emissions. For drought, the popular indicator of consecutive dry days (CDD) is used. Most models project a decrease of CDD, with the MME rate of −0.2 days per decade, implying mitigation of drought in China in the future. Concurrently, most models (13 out of 20) show a negative sensitivity for drought to CO2 warming under both DCO2 and TCO2, with values of −1.6%/°C and −1.2%/°C, respectively (Table 1). However, a spatial inconsistency can be observed for the CDD changes. The sensitivity is highly positive for an increased risk of drought over southern China and southwest Xinjiang, and negative for a decreased risk in other regions; specifically, in some parts of Tibet and Northeast China (Fig. 2e). In contrast to CDD, there is an overall increase in RX5day (maximum five-day precipitation) in China (Fig. 2f), which is a typical indicator of flood occurrence. A significant increase of RX5day is found in southern China, which implies that this region is likely to experience increased risks in terms of both flood and drought occurrences in the future due to increased CO2. Meanwhile, there will be more floods but fewer droughts in other regions of China.

4

Conclusions

In this study, the sensitivities of the climate means and associated extremes to warming are investigated on the basis of a 140-yr experiment with a prescribed 1% per year increase in CO2 emissions in CMIP5 simulations. The analysis indicates that regional warming in China is much faster than the global average due to CO2 emissions. Additionally, the CMIP5 models project a robust response of precipitation characteristics to CO2 warming, featuring an overall increase for China. Under the scenario of a 1% CO2 increase per year, the annual mean precipitation presents a quasi-steady response, with a positive sensitivity of 2.2%/°C. The responses of changes in climate extremes are generally much stronger than those in climate means. For the temperature-related extremes, warm events often present a high positive sensitivity for a significant increase, while cold events show a negative sensitivity for a decrease. For example, all models project a significant increase in the temperatures of the hottest day and coldest day of the year due to CO2 warming, but the temperature of the coldest day exhibits a stronger response than the hottest day, leading to a decrease of DTR. FD and ID are reduced over the whole of China, while SU and TR present significant increases. MMEs project that the probability of occurrence of FD and ID will decrease by about 4.7% and 7.0%, respectively, and SU and TR will increase by 12.4% and 26.0%, respectively, in China per 1°C of warming due to CO2 emissions. Similar features are found in the other temperature-related extreme indices. Additionally, analyses further indicate that the precipita-

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tion-related extremes will become more extreme due to CO2 warming in China. Most indices are projected to present a high positive sensitivity to warming. Furthermore, more extreme precipitation events generally show higher sensitivity. However, due to the significant increase of precipitation, most models project a decreased risk of drought and an increased risk of flood, except for southern China, which is projected to experience more drought and flood events in the future due to CO2 warming. These change sensitivities generally remain approximately constant under DCO2 and TCO2, indicating that the climate extreme response may also reach a quasisteady state under both scenarios. Notably, a large intermodel spread can be observed in the sensitivity calculation of most extreme indices, but most models show consistency in the sign of change, which makes the changes in extreme events much more believable. Acknowledgements. The authors are grateful to the two anonymous reviewers for their valuable comments and helpful advice. We also acknowledge the World Climate Research Programme’s working group on coupled modeling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. This research was jointly supported by the National Basic Research Program of China (Grant No. 2012CB955401), the National Natural Science Foundation of China (Grant No. 41305061), and the “Strategic Priority Research Program—Climate Change: Carbon Budget and Relevant Issues” of the Chinese Academy of Sciences (Grant No. XDA05090306).

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