globalchange  > 气候变化事实与影响
DOI: doi:10.1038/nclimate2805
论文题名:
Large rainfall changes consistently projected over substantial areas of tropical land
作者: Robin Chadwick
刊名: Nature Climate Change
ISSN: 1758-743X
EISSN: 1758-6863
出版年: 2015-09-28
卷: Volume:6, 页码:Pages:177;181 (2016)
语种: 英语
英文关键词: Projection and prediction ; Climate-change impacts ; Climate-change mitigation ; Hydrology
英文摘要:

Many tropical countries are exceptionally vulnerable to changes in rainfall patterns, with floods or droughts often severely affecting human life and health, food and water supplies, ecosystems and infrastructure1. There is widespread disagreement among climate model projections of how and where rainfall will change over tropical land at the regional scales relevant to impacts2, 3, 4, with different models predicting the position of current tropical wet and dry regions to shift in different ways5, 6. Here we show that despite uncertainty in the location of future rainfall shifts, climate models consistently project that large rainfall changes will occur for a considerable proportion of tropical land over the twenty-first century. The area of semi-arid land affected by large changes under a higher emissions scenario is likely to be greater than during even the most extreme regional wet or dry periods of the twentieth century, such as the Sahel drought of the late 1960s to 1990s. Substantial changes are projected to occur by mid-century—earlier than previously expected2, 7—and to intensify in line with global temperature rise. Therefore, current climate projections contain quantitative, decision-relevant information on future regional rainfall changes, particularly with regard to climate change mitigation policy.

Climate change is expected to drive changes in tropical rainfall by affecting both atmospheric moisture and circulation8, 9, 10, 11. In the absence of circulation change, the enhanced capacity of warmer air to contain moisture would lead to increased PE (precipitation minus evaporation) in already wet regions and decreases in dry regions; the so-called ‘wet-get-wetter, dry-get-drier hypothesis8, 9, 11. This mode of change is present in both climate model simulations9, 11, 12, 13 and observed trends13, 14, and hence is an important validation of model fidelity, but is seen only when very large area averages are used5, 6. At the regional scales more relevant to climate change impacts, the wet-get-wetter, dry-get-drier paradigm is not a good predictor of rainfall change in projections5, 6, 15 or observations16, 17, and projections of future regional rainfall change vary widely across climate models2, 3, 18, 19.

Instead, the dominant driver of regional rainfall change in the tropics is the occurrence of shifts in the position of wet regions5, 6. These spatial shifts can cause both increases and decreases in rainfall, and are illustrated here with two very different climate model projections of future precipitation (Fig. 1a, b). Large shifts occur in both models, and in each model are generally coherent in sign over areas large enough to affect whole countries or regions, but the locations of shifts differ greatly between the two. Uncertainty over which regions will experience these shifts, and to what extent, is the main cause of spread in regional rainfall projections5, 6, with even the sign of change uncertain in some regions2, 3. This uncertainty impedes planning for adaptation to climate change. For global climate change mitigation policy, however, the precise location of large rainfall changes may be less important than whether or not they will occur, combined with an estimate of their magnitude and areal coverage.

Figure 1: Tropical land precipitation changes in two climate models, and observations of Sahel drought.
Tropical land precipitation changes in two climate models, and observations of Sahel drought.

a,b, Precipitation change for 2071–2100 minus 1971–2000, under the RCP8.5 emissions scenario for GFDL-ESM2M (a) and HadGEM2-ES (b). c, CRU observed rainfall change (%) for 1968–1997 minus 1938–1967. Desert and sea regions are masked in white. Outlined areas in c indicate Sahel and northwest Australia regions, and the black line contour indicates the upper rainfall threshold for semi-arid regions (2.2mmd−1).

Data.

CMIP5 climate model data (see List of Climate Models, below) for the four RCP scenarios (2006–2100), historical (1860–2005) and pre-industrial control experiments were regridded to a 2.5° grid. The domain was restricted to 30° N–30° S, and a sea mask applied. A sea mask from the HadGEM2-ES model was used, with areas with 100% sea fraction set to 0 and other areas set to 1. This was regridded to 2.5° resolution, and any areas with a regridded mask value of <0.5 were taken as sea points and masked for all models (see Fig. 1). This choice of mask retains a large number of coastal points in the data set, but the results of this study are not sensitive to this, and were very similar when a much stricter sea mask was applied as a test. This is because rainfall change over coastal grid points does not seem to behave in a systematically different way from inland grid points. Only one ensemble member was used for each RCP model run, although the effect of this was tested (see Estimation of internal climate variability, below).

CRU TS (Climate Research Unit Time Series) 3.21 observed rainfall data32 were regridded to 2.5° and restricted to 30° N–30° S. For the calculation of biases shown in Supplementary Fig. 1, GPCP (Global Precipitation Climatology Project) version 2 data33 were also used for comparison, but this made little difference and so is not shown. For the Sahel drought analysis the regions used were: Sahel (10° N–18° N, 20° W–35° E) and northwest Australian (25° S–11° S, 112°E–142°E)—see Fig. 1c.

As large percentage rainfall anomalies are commonly projected in desert regions, but correspond only to small absolute changes that are unlikely to have much impact in these largely uninhabited areas, a desert mask was used to remove these regions from the analysis, with a threshold of 200mmyr−1 (0.55mmd−1). To identify semi-arid and rainforest regions, values of respectively 200–800mmyr−1 (0.55–2.2mmd−1) and >1,640mmyr−1 (4.5mmd−1) were used34, 35. Results were found to be insensitive to the value of these thresholds (values of 100mmyr−1 each side of each threshold were tested). Thresholds were applied to each model or observational data set based on their own rainfall climatologies, and the multi-model mean thresholds are shown in Supplementary Fig. 1. As a result of model biases, the semi-arid and rainforest rainfall regimes defined here for each model may not always be located in exactly the same regions as those in observations, but are nevertheless useful for indicating how rainfall changes in each model within each type of rainfall regime. An alternative method of applying thresholds based on the observed CRU climatology was also tested, and the results of this study were not found to be sensitive to this choice.

Annual mean totals were used instead of seasonal totals because annual mean percentage changes provide a more robust measure of large rainfall changes than seasonal percentage changes. The tropics-wide distribution of seasonal percentage change can be dominated by large dry-season percentage changes that correspond to only small absolute changes, and are unlikely to have major impacts. Seasonal changes can also correspond to changes in the timing of rainy seasons which, although important, do not necessarily mean that total rainfall amount has changed. Annual mean percentage changes are not affected by either of these issues.

Results are sensitive to the choice of grid-box size: models with higher-resolution native grids have greater percentage area coverage of large rainfall change when the data are analysed at this higher resolution, than after averaging to 2.5°. This is expected, as large rainfall changes are more common at higher spatial resolution owing to the smoothing effects of averaging. The choice of 2.5° used here seems to be sensible, as it is at the coarse end of CMIP5 simulations, and so allows them to be compared at the same scale without any unphysical regridding of coarse simulations to a higher resolution.

Estimation of internal climate variability.

The distribution of rainfall changes expected between two 30 year means from internal climate variability was estimated using long climate model control runs under pre-industrial greenhouse gas forcing. Two hundred and forty years were used from each model control run, each containing 8 consecutive 30 year periods. Mean variability was estimated for each model by taking the difference between each pair of non-consecutive 30 year periods, separating the resulting anomaly grid points into rainfall threshold bins (for example, >10% increase) and then finally dividing the number in each bin by the number of pairs of time periods. Only non-consecutive periods were used in case consecutive periods were more highly correlated with one another than non-consecutive ones.

This method makes the assumption that 30 year mean internal rainfall variability remains the same under forcing. To test this, an alternative method was also used, where models with at least two ensemble members of RCP4.5 were used. In this case, natural variability for each model was estimated by taking the difference between 30 year means of the projected future rainfall change of the two ensemble members. The resulting distribution of model estimates was very similar to that obtained using control runs, although with slightly wider uncertainty ranges—probably due to only two ensemble members being used as compared with eight different time periods in the control run method . Therefore, we consider that the control run method of variability estimation is robust. The relatively low tropical rainfall variability found here on 30 year timescales is also consistent with a previous estimate6.

Reliability of model estimates of multi-decadal variability.

Multi-decadal internal climate variability has been estimated here from climate models. To try to assess how valid these model estimates are, 30 year CMIP5 variability over the twentieth century was compared with corresponding variability in observed CRU data across tropical land. This exercise is hindered by the fact that both anthropogenic and natural forcing are present in both reality and historical model runs, and so the comparison measures the response to forcing as well as the range of natural variability. Observational error may also be substantial, particularly in the early part of the century where observations in the tropics are sparse.

Three consecutive 30 year means (1916–1945, 1946–1975, 1976–2005) were used from both observations and historical model simulations. Combined forced and internal variability was estimated by taking the difference at each grid point between each pair of time periods, separating into threshold bins, and then dividing the number in each bin by the number of time-period pairs (Supplementary Fig. 2). CRU variability is outside (above) the range of model estimates for changes >10%, and just inside the range for changes >20%. It is unclear what proportion of this discrepancy is due to the response to forcing, natural variability or observational uncertainty.

If models do systematically underestimate either variability or the historical response to forcing, then this could also have implications for the reliability of their future response to forcing. One possibility is that some future 30 year mean rainfall changes could be larger than projected at present.

List of climate models.

ACCESS1-0, ACCESS1-3, BCC-CSM1-1, BCC-CSM1-1-M, BNU-ESM, CanESM2, CCSM4, CESM1-BGC, CESM1-CAM5, CMCC-ESM, CMCC-CM, CMCC-CMS, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, FGOALS-G2, FIO-ESM, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H_p1, GISS-E2-H_p2, GISS-E2-H_p3, GISS-E2-H-CC, GISS-E2-R_p1, GISS-E2-R_p2, GISS-E2-R_p3, GISS-E2-R-CC, HadGEM2-AO, HadGEM2-CC, HadGEM2-ES, INM-CM4, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MPI-ESM-MR, MPI-ESM-P, MRI-CGCM3, NorESM1-M, NorESM1-ME.

Of these 44 models, 39 had output for RCP8.5, 25 for RCP6.0, 42 for RCP4.5, and 32 for RCP2.6. Control run data were available for 39 of the models.

  1. IPCC Summary for Policymakers in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).
  2. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
  3. McSweeney, C. F. & Jones, R. G. No consensus on consensus: The challenge of finding a universal approach to measuring and mapping ensemble consistency in GCM projections. Climatic Change 119, 617629 (2013).
  4. Neelin, J. D., Munnich, M., Su, H., Meyerson, J. E. & Holloway, C. E. Tropical drying trends in global warming models and observations. Proc. Natl Acad. Sci. USA 103, 61106115 (2006).
  5. Chadwick, R., Boutle, I. & Martin, G. Spatial patterns of precipitation change in CMIP5: Why the rich do not get richer in the tropics. J. Clim. 26, 38033822 (2013).
  6. Kent, C., Chadwick, R. & Rowell, D. P. Understanding uncertainties in future projections of regional precipitation. J. Clim. 28, 43904413 (2015).
  7. Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183187 (2014).
URL: http://www.nature.com/nclimate/journal/v6/n2/full/nclimate2805.html
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/4571
Appears in Collections:气候变化事实与影响
科学计划与规划
气候变化与战略

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Robin Chadwick. Large rainfall changes consistently projected over substantial areas of tropical land[J]. Nature Climate Change,2015-09-28,Volume:6:Pages:177;181 (2016).
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