2. Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China;
3. School of Earth and Ocean Sciences, University of Victoria, Victoria BC, Canada;
4. Department of Meteorology, COMSATS Institute of Information Technology, Islamabad, Pakistan
The role of black carbon (BC) in modulating the regional climate of South Asia has been identified in a number of previous modeling studies (e.g.,Ramanathan et al., 2005;Lau et al., 2006;Meehl et al., 2008;Wang et al., 2009;Mahmood and Li, 2013;Lau, 2016). A provisional consensus is that BC may be a primary contributor to the observed pre-monsoonal enhancement, and monsoonal decline, in rainfall (e.g.,Meehl et al., 2008, Figs. 9 and 10). The significant contribution of BC in modulating regional and/or remote climate arises from the fact that the induced strong heating in the lower troposphere can influence the local climate through changing atmospheric stability and/or general circulation systems (Jacobson, 2001;Menon et al., 2002;Jiang et al., 2013;Mahmood and Li, 2013,2014;Liao and Shang, 2015). While the physical mechanisms are still under debate, evidence suggests that aerosols may play a more important role in influencing South Asian climate than greenhouse gases (e.g.,Bollasina et al., 2011). Since some models in the World Climate Research Program's Coupled Model Intercomparison Project Phase 3 (CMIP3) historical simulation experiments include BC forcing, while others do not (Meehl et al., 2007), a comparison between the two groups' simulations may reveal the climatological impact of BC, instead of conducting additional experiments. One question we may naturally want to ask is: Do those CMIP3 models that include BC forcing produce relatively more realistic results? If so, what are the spatial and temporal distributions of projected precipitation from these models, given that the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) projected a homogeneous rainfall enhancement in South Asia based on the multi-model ensemble mean (MME) of 21 of the CMIP3 models (Christensen et al., 2007, Fig. 11.9)? These considerations motivated the present study, which began by comparing the 20th century simulations of South Asian climate in two groups of models with and without the effect of BC. The results suggested that the models considering the effect of BC reproduce the observed climate more realistically. Therefore, the future South Asian climate was then projected by using these models, based on the IPCC AR4 projection simulations. The results projected a negative precipitation anomaly over central India in the MME of the models that include the effect of BC: a result that is in contrast to the homogeneous rainfall increase projected by the MME of the 21 IPCC AR4 models.2 Data and methods
The observational rainfall datasets used in this study include: the land-only precipitation from the Climate Research Unit (CRU) of the University of East Anglia (CRU TS3.1;Mitchell and Jones, 2005); the University of Delaware terrestrial precipitation, version 2.01 (Legates and Willmott, 1990); and global precipitation from the Global Precipitation Climatology Project (GPCP), version 2.2 (Adler et al., 2003). The mean sea level pressure (SLP) was from the ECMWF's ERA-40 reanalysis (Uppala et al., 2005).
Two sets of CMIP3 model simulations were used: one from the 20th century simulations (20C3M) and the other from the 21st century simulations with SRESA1B scenarios. The simulations of a total of 22 models were analyzed according to two groups: one involving 9 models that consider the effect of BC (in short, CMIP3_BC), and the other involving 13 models that do not consider the effect of BC (in short, CMIP3_NOBC) (see Table 1). The methodology for grouping the models was similar to that of Purich and Son (2012) and Allen et al. (2012), but just based on BC.Purich and Son (2012) grouped the models based on considerations of ozone when studying the influence of ozone depletion and recovery on Southern Hemisphere climate.Allen et al. (2012) grouped the CMIP3 models based on BC and tropospheric ozone when studying the two forcings' contributions to Northern Hemispheric tropical expansion.
|1 CSIRO Mk3.0 (CSIRO Atmospheric Research Group, Australia)||1 CCSM3 (National Center for Atmospheric Research, USA)|
|2 CSIRO Mk3.5 (CSIRO Atmospheric Research Group, Australia)||2 CNRM-CM3 (Meteo France/Centre National de Recherches Météorologiques, France)|
|3 ECHAM5_MPI (Max Plank Institute for Meteorology, Germany)||3 GFDL CM2.0 (U.S. Dept. of Commerce/NOAA/Geophysical Fluid Dynamics Laboratory, USA)|
|4 FGOALS-g1.0 (Institute of Atmospheric Physics, China)||4 GFDL CM2.1 (U.S. Dept. of Commerce/NOAA/Geophysical Fluid Dynamics Laboratory, USA)|
|5 IPSL-CM4 (L'Institut Pierre Simon Laplace, France)||5 GISS-E2-H (NASA/Goddard Institute for Space Studies, USA)|
|6 INM-CM3.0 (Institute for Numerical Mathematics, Russia)||6 GISS-E2-R (NASA/Goddard Institute for Space Studies, USA)|
|7 GISS-AOM (NASA/Goddard Institute for Space Studies, USA)||7 MIROC3.2 (hires) [Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies and Frontier Research Center for Global Change, Japan]|
|8 NCAR_PCM1 (National Center for Atmospheric Research, USA)||8 MIROC3.2 (medres) [Center for Climate System Research (University of Tokyo), National Institute for Environmental Studies and Frontier Research Center for Global Change, Japan]|
|9 HadCM3 (Hadley Centre for Climate Prediction and Research/Met Office, UK)||9 HadGEM1 (Hadley Centre for Climate Prediction and Research/Met Office, UK)|
|10 CGCM3.1 [T47] (Canadian Centre for Climate Modeling & Analysis, Canada)|
|11 CGCM3.1 [T63] (Canadian Centre for Climate Modeling & Analysis, Canada)|
|12 INGV_ECHAM4 (Instituto Nazionale di Geofisica e Vulcanologia, Italy)|
|13 MRI-CGCM2.3.2 (Meteorological Research Institute, Japan)|
To assess the potential summer (June–September, JJAS) precipitation responses in the future, the results of the SRESA1B experiment were subtracted from the 1950–99 climatology of the 20C3M experiment.Seager et al. (2007) calculated similar climatological differences to assess the possible future aridity in southwestern North America. Due to the different spatial resolutions of the various models, bilinear interpolation was performed to interpolate all the modeled precipitation onto a uniform 5°×5° grid. Only the first run (i.e., RUN1) of the model output was analyzed for both types of experiments (i.e., 20C3M and SRESA1B), except for two models (FGOALS-g1.0 and GISS-E2-H) in which RUN2 of the SRESA1B experiment was used due to the unavailability of RUN1.3 Results and discussion 3.1 Reasonableness of the 20C3M simulations
We began our analysis by plotting 5-yr running means of observed and simulated summer precipitation averaged over (25°–35°N, 65°–95°E) for the period 1950–99 (Fig. 1a), along with the corresponding 50-yr statistics (Fig. 1b). A key feature in Fig. 1 is the CMIP3_BC's closer resemblance than CMIP3_NOBC to the observation. The fact that the modeled precipitation in CMIP3_BC has amplitudes similar to the observed signifies the potential effect of BC on South Asian climate, as found in many previous studies (e.g.,Menon et al., 2002;Mahmood and Li, 2013,2014). However, both model groups failed to reproduce the observed precipitation trend for the selected domain.
The results shown in Fig. 1 provide interesting information related to the dominance of CMIP3_BC over CMIP3_NOBC in reproducing the observed summer precipitation. However, this is likely to be domain dependent, and therefore, the spatial distributions of the observed and simulated JJAS climatologies for the period 1980–99 were compared (Fig. 2). In observations, the strongest and most spatially extended precipitation center is over Myanmar, Bangladesh, and eastern India (Figs. 2a–c). Another prominent precipitation center is near the western side of the Indian Peninsula (Figs. 2a–c). The fact that the two model groups simulated these strong precipitation centers comparatively better may provide a basic assessment for the reasonableness of the simulations in CMIP3_BC and CMIP3_NOBC.Annamalai et al. (2007) used a similar approach to test the performance of several individual models. A general comparison between the observed (Figs. 2a–c) and simulated (Figs. 2d and 2e) precipitation suggested that there was a marked discrepancy between the two groups in reproducing the above observed precipitation centers. Despite that, it appeared that the underestimation was much higher in the case of CMIP3_NOBC (see their difference depicted in Fig. 2f). From Fig. 2f, almost all of the land regions, including the two strong precipitation centers in observations, were comparatively stronger in the case of CMIP3_BC, and thus featured less severe underestimation. Interestingly, the strongest difference between CMIP3_BC and CMIP3_NOBC (about 4 mm day–1) was located approximately in the strongest precipitation region in the observation. In the northwestern Indian Ocean (off the Somali Coast), there was a relatively stronger precipitation bias in CMIP3_BC. Such a model bias was investigated recently by Bollasina and Ming (2013), who attributed it to the extra sensitivity of models to the meridional sea surface temperature gradients. Also, for southern Indo-China, both groups appeared to overestimate the observed precipitation. Nevertheless, it may be argued that, at least for the South Asian region, the MME of the CMIP3_BC models may represent observed precipitation comparatively better than that of the CMIP3_NOBC models.
Another metric used for evaluating model performance was the 1980–99 JJAS mean SLP, which in observations was represented by ECMWF (ERA-40) reanalysis data (Fig. 3). For the observed climatology, one important feature is the existence of a low pressure center over Pakistan and western India (Fig. 3a). This low pressure is further extended up to the Bay of Bengal in the east and to the eastern Arabian Peninsula in the west. Another important feature is the relatively higher pressure over the Tibetan Plateau (TP). So, we further evaluated the performance of the two model groups based on how well they performed in reproducing this dipole-like SLP structure. First, all the models in both groups appeared to extend the low pressures too far north over the TP (Figs. 3b and 3c), and thus there was less of a blocking effect of the TP, which may have contributed to the overall underestimation in simulated precipitation in South Asia and an overestimation in southern Indo-China. Again, the difference between the two model groups (Fig. 3d) suggested that CMIP3_BC had a dipole-like pattern, with positive SLP over the TP and negative SLP over the subcontinent. From these analyses, the CMIP3_BC simulations arguably correlated better than those of CMIP3_NOBC with the observed/reanalysis results in the South Asia region.
However, it is important to note that both groups struggled with reproducing the spatial distributions of observed precipitation and SLP. In addition, although the focus of this study was on the potential influence of BC on climatological precipitation in South Asia, it is highly likely that other natural and anthropogenic forcings also play a role in modulating observed precipitation. Similarly, the missing indirect effects of aerosols could contribute too. Furthermore, the inherent differences in individual model structures and assumptions for parameterizations may also influence the model simulations.3.2 Future precipitation projections
We evaluated the projection of future precipitation based on SRESA1B simulations. Although there are several uncertainties associated with model simulations of future climate (e.g.,Knutti, 2008) and questionable credibility due to model discrepancies and the assumptions about future physical conditions (Schwartz et al., 2007;Tebaldi and Knutti, 2007;Knutti, 2008), they nevertheless may provide a basic assessment (or a calculated guess) on the possible future conditions (e.g.,Christensen et al., 2007;Seager et al., 2007;Mariotti et al., 2008;Purich and Son, 2012). Thus, our next objective was to evaluate future precipitation in the simulations with and without BC, which may be useful when interpreting the possible future climate conditions in the subcontinent.
Figure 4 shows the JJAS mean precipitation difference (i.e., 2050–99 minus 1950–99) for land-only regions. The difference derived from CMIP3_BC (Fig. 4a) suggested that the mean summer precipitation may increase in Bangladesh, eastern and southern India, and northern Pakistan, along with a relative increase in aridity in central India; while that from CMIP3_NOBC suggested increasingly wet conditions over the whole of South Asia (Fig. 4b). These results highlighted a major difference over central India, with a relatively dryer future according to CMIP3_BC (Fig. 4a) versus a wetter future according to CMIP3_NOBC (Fig. 4a). This future summer dryness in central India according to CMIP3_BC may reach up to a maximum of 8% relative to the mean conditions during 1950–99 (Fig. 5). Interestingly, such dry conditions appear to be in line with the observed drying trends over this region during the last 50 years of the 20th century (cf.Figs. 5 and 6). Another notable point is the distribution of the future precipitation anomaly over Pakistan, where both model groups suggested wetness, although the spatial distribution appeared more similar to the observed trends, especially for northern Pakistan, in the past decades in CMIP3_BC. CMIP3_BC suggested up to a 20% increase in precipitation in northern Pakistan, which is interesting in terms of the past moistening trends in some of the observations (e.g., in CRU; cf.Figs. 5 and 6). Similarly, CMIP3_NOBC also suggested a positive sign in this region, but its center shifted towards the southern coastal areas. However, it should be noted that these trends and anomalous patterns are not directly comparable, since they span over different timescales and are therefore subject to change. Additionally, the current study is limited in terms of identifying the sole source of such distributions, which could be the result of model artifacts and/or missing forcings in the simulations.
Comparing the present results with those shown in IPCC AR4 (Christensen et al., 2007, Fig. 11.9), CMIP3_BC presents a contrasting pattern over central India: a potential future of increasingly dry rather than wet conditions over the whole of the South Asia region. Moreover, the heterogeneity in the spatial detail of the projected precipitation over South Asia in our analysis suggests that, perhaps, it may not be wise to simply calculate the area mean for a region as large as the whole South Asian domain, as shown by Giorgi and Bi (2005,Fig. 1), to predict future regional precipitation.Giorgi and Bi (2005) concluded an increase in future precipitation over South Asia in the wet season, which can obscure the more complicated spatial patterns as shown in the current analysis.4 Concluding remarks
We investigated the CMIP3 simulations of South Asian past and future summer precipitation by dividing the models into two groups based on inclusion/exclusion of BC in these simulations. First, the results from the 20C3M experiment were compared with observations to assess which model group resembles the observations more closely. The comparisons suggested that the MME of the group with BC (CMIP3_BC) represents the observed situation relatively better than that of the group without BC (CMIP3_NOBC). Next, we investigated what these model groups suggest in terms of future precipitation under the SRESA1B experiment. The most interesting result was the contrasting anomalous future precipitation projection in central India, where CMIP3_BC suggested increasingly dry conditions while CMIP3_NOBC implied increasingly wet conditions. The projected pattern of the former was also in contrast with that established by the MME of 21 models in IPCC AR4 and in previous studies (e.g.,Giorgi and Bi, 2005). Based on this analysis of CMIP3 models, we can expect more anomalously complex spatial precipitation patterns in South Asia, rather than a uniform increase in future precipitation.
Readers will undoubtedly be wondering why the simulations of CMIP5, instead of CMIP3, were not used in the present analysis. The answer is that all historical simulations from CMIP5 models include BC forcing. Thus, they were unsuitable for the present comparison. Nevertheless, the present results provide indications about the complexity of future projections of South Asian summer rainfall in these models.
Possible caveats to the present analysis include the different model configurations, resolutions, and basic atmospheric physics in individual models, all of which can contribute differently to the simulated results (e.g.,Mahmood et al., 2016). Additionally, from the current study alone, it is not possible to explicitly assign BC as the sole contributor to the simulated results (as discussed above):a matter that requires more objectively based multi-model simulations.
Acknowledgements . We acknowledge the modeling groups, the PCMDI, and the WCRP's Working Group on Climate Modeling, for making the WCRP CMIP3 multi-model dataset available. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.
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