1 2013 Vol: 3(1):52-58. DOI: 10.1038/nclimate1633

Increasing drought under global warming in observations and models

Historical records show increased aridity over many land areas since 1950. This study looks at observations and model projections from 1923 to 2010, to test the ability of models to predict future drought conditions. Models are able to capture the greenhouse-gas forcing and El Nino-Southern Oscillation mode for historical periods, which inspires confidence in their projections of drought.

Mentions
Figures
Figure 1: Trend maps for precipitation and sc_PDSI_pm and time series of percentage dry areas. Long-term trends from 1950 to 2010 in annual mean a, observed precipitation2 and b, calculated sc_PDSI_pm using observation-based forcing2. The stippling indicates the trend is statistically significant at the 5% level, with the effective degree of freedom computed using the method of ref. 30. Note a change of 0.5 in the sc_PDSI_pm is significant in the sense that a value of PDSI between −0.5 to −1.0, −1.0 to −2.0, −2.0 to −3.0 and −3.0 to −4.0 indicates, respectively, a dry spell, mild drought, moderate drought and severe drought2. c, Smoothed time series of the drought area as a percentage of global land areas based on the sc_PDSI_pm computed with (red line) and without (green line) the observed surface warming. The drought areas are defined locally as the cases when sc_PDSI_pm is below the value of the twentieth percentile of the 1950–1979 period (results are similar for drought defined as PDSI<−2.0 and for using a longer base period from 1948 to 2010). Figure 2: Future changes in soil moisture and sc_PDSI_pm. a, Percentage changes from 1980–1999 to 2080–2099 in the multimodel ensemble mean soil-moisture content in the top 10 cm layer (broadly similar for the whole soil layer) simulated by 11 CMIP5 models under the RCP4.5 emissions scenario. Stippling indicates at least 82% (9 out of 11) of the models agree on the sign of change. b, Mean sc_PDSI_pm averaged over 2090–2099 computed using the 14-model ensemble mean climate (including surface air temperature, precipitation, wind speed, specific humidity and net radiation) from the CMIP5 simulations under the RCP4.5 scenario. A sc_PDSI_pm value of −3.0 or below indicates severe to extreme droughts for the present climate, but its quantitative interpretation for future values in b may require modification. Figure 3: Temporal and spatial patterns of the MCA2 mode for SST and sc_PDSI_pm from observations and models. a, Temporal (black line for SST, red line for sc_PDSI_pm, on the left-side ordinate) and b–e, spatial expansion coefficients of the second leading mode from a MCA of 13-point moving-averaged monthly SST from observations27 and sc_PDSI_pm computed from observational forcing (a–c) and from 14 CMIP5 model ensemble-mean simulations (d,e) for 1923–2010 (observational data are unreliable for earlier years). The blue line in a is the observed Nino3.4 SST index (right-side ordinate) obtained from http://www.esrl.noaa.gov/psd/forecasts/sstlim/Globalsst.html (for 1950–2010) and from http://www.cgd.ucar.edu/cas/catalog/climind/TNI_N34/index.html#Sec5 (for pre-1950 years, rescaled to match the National Oceanic and Atmospheric Administration index over the 1950–2007 common data period). In a, SFC is the squared fractional covariance explained by the MCA mode and the r1 and r2 are the correlation coefficients between, respectively, the black and red, and the black and blue curves. pVar is the percentage variance explained by the MCA mode in b–e. The spatial pattern correlation coefficient is 0.81 between b and d and 0.48 between c and e, both are statistically significant at the 1% level. Figure 4: Temporal and spatial patterns of the MCA1 mode for SST and sc+PDSI_pm from observations and models. a–f, The blue line (right-side ordinate) is the global mean surface temperature from observations31 in a and global mean surface air temperature from the models in b, which is the temporal coefficient of the MCA1 for the model SST (black) and sc_PDSI_pm (red). The correlation between the black (red) lines in a and b is 0.85 (0.86) and the regression coefficient (with the observation as the predictor) between the SST (sc_PDSI_pm) anomalies represented by the MCA1 mode for the observation and models is 0.9119 (0.9566). The product of the temporal (a,b) and corresponding spatial (c–f) coefficients is the SST and PDSI anomaly represented by the MCA mode, with red areas experiencing warming (for SST) and drying (for PDSI) and blue areas for cooling and wetting. Figure 5: Temporal and spatial patterns of the CMIP5 model for SST and sc_PDSI_pm from 1950 to 2099 under the RCP4.5 future emissions scenario.
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References
  1. Dai, A. Drought under global warming: A review WIREs Climatic Change 2, 45-65 (2011) .
    • . . . Historical records of precipitation, streamflow and drought indices all show increased aridity since 1950 over many land areas1, 2 . . .
    • . . . Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century . . .
    • . . . There are, however, large differences in the observed and model-simulated drying patterns1, 2, 6 . . .
    • . . . Although the historical and future aridity changes have been discussed in previous studies1, 2, 3, 4, 5, 6, 7, there still is a need to validate the historical changes and reconcile them with model projections . . .
    • . . . Here I focus on the large-scale drying trends in precipitation, streamflow and soil moisture fields, which are commonly used to quantify, respectively, meteorological, hydrologic and agricultural drought1 . . .
    • . . . These patterns are also broadly comparable to those seen in observed streamflow trends since 1948 in the world’s main river basins1, 17 . . .
    • . . . The sc_PDSI_pm maps for future decades based on the CMIP3 were briefly discussed in ref.  1, but were not compared with the simulated soil-moisture and historical sc_PDSI_pm changes . . .
    • . . . For model-predicted twenty-first-century climates, the use of the Penman–Monteith potential evapotranspiration greatly reduces the drying trend1. . . .
  2. Dai, A. Characteristics and trends in various forms of the Palmer Drought Severity Index (PDSI) during 1900–2008 J. Geophys. Res. 116, D12115 (2011) .
  3. Wang, G. L. Agricultural drought in a future climate: Results from 15 global climate models participating in the IPCC 4th assessment Clim. Dynam. 25, 739-753 (2005) .
    • . . . Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century . . .
    • . . . Although the historical and future aridity changes have been discussed in previous studies1, 2, 3, 4, 5, 6, 7, there still is a need to validate the historical changes and reconcile them with model projections . . .
    • . . . Similar changes (but with some regional differences) are also seen in CMIP3 models3, 4 (Supplementary Fig . . .
  4. Sheffield, J.; Wood, E. F. Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations Clim. Dynam. 31, 79-105 (2008) .
    • . . . Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century . . .
    • . . . Although the historical and future aridity changes have been discussed in previous studies1, 2, 3, 4, 5, 6, 7, there still is a need to validate the historical changes and reconcile them with model projections . . .
    • . . . Similar changes (but with some regional differences) are also seen in CMIP3 models3, 4 (Supplementary Fig . . .
  5. Rind, D.; Goldberg, R.; Hansen, J.; Rosenzweig, C.; Ruedy, R. Potential evapotranspiration and the likelihood of future drought J. Geophys. Res. 95, 9983-10004 (1990) .
    • . . . Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century . . .
    • . . . Although the historical and future aridity changes have been discussed in previous studies1, 2, 3, 4, 5, 6, 7, there still is a need to validate the historical changes and reconcile them with model projections . . .
    • . . . The PDSI is calculated from a water-balance model forced with observed precipitation and temperature and has been widely used in monitoring drought development over the USA, palaeoclimate reconstruction15 and studying aridity changes2, 5, 6, 16 . . .
  6. Burke, E. J.; Brown, S. J. Evaluating uncertainties in the projection of future drought J. Hydrometeorol. 9, 292-299 (2008) .
    • . . . Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century . . .
    • . . . Although the historical and future aridity changes have been discussed in previous studies1, 2, 3, 4, 5, 6, 7, there still is a need to validate the historical changes and reconcile them with model projections . . .
    • . . . The PDSI is calculated from a water-balance model forced with observed precipitation and temperature and has been widely used in monitoring drought development over the USA, palaeoclimate reconstruction15 and studying aridity changes2, 5, 6, 16 . . .
  7. Seager, R. Model projections of an imminent transition to a more arid climate in southwestern North America Science 316, 1181-1184 (2007) .
    • . . . Analyses of model-simulated soil moisture3, 4, drought indices1, 5, 6 and precipitation-minus-evaporation7 suggest increased risk of drought in the twenty-first century . . .
    • . . . Although the historical and future aridity changes have been discussed in previous studies1, 2, 3, 4, 5, 6, 7, there still is a need to validate the historical changes and reconcile them with model projections . . .
  8. Giannini, A.; Saravanan, R.; Chang, P. Oceanic forcing of Sahel rainfall on interannual to interdecadal time scales Science 302, 1027-1030 (2003) .
    • . . . Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large influences on land precipitation and the inability of the coupled models to reproduce many observed regional precipitation changes is linked to the lack of the observed, largely natural change patterns in sea surface temperatures in coupled model simulations13 . . .
    • . . . In contrast, precipitation decreases over Africa, southeast Asia, eastern Australia and southern Europe are the primary cause for the drying trend over there, and the long-term trends and decadal tomultidecadal variations in sea surface temperature (SST) are a major driver for many of the precipitation changes8, 9, 10, 11, 12 . . .
    • . . . The drying trend since 1950 over the Sahel results mainly from the decreases in summer rainfall from the 1950s to the mid-1980s (ref. 22) that are related to the observed large warming in the South Atlantic Ocean relative to the North Atlantic Ocean and the steady warming over the Indian Ocean8, 11, together with significant contributions from dynamic vegetation feedback23, 24, which is not simulated in the CMIP models . . .
  9. Schubert, S. D.; Suarez, M. J.; Pegion, P. J.; Koster, R. D.; Bacmeister, J. T. On the cause of the 1930s Dust Bowl Science 303, 1855-1859 (2004) .
    • . . . Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large influences on land precipitation and the inability of the coupled models to reproduce many observed regional precipitation changes is linked to the lack of the observed, largely natural change patterns in sea surface temperatures in coupled model simulations13 . . .
    • . . . In contrast, precipitation decreases over Africa, southeast Asia, eastern Australia and southern Europe are the primary cause for the drying trend over there, and the long-term trends and decadal tomultidecadal variations in sea surface temperature (SST) are a major driver for many of the precipitation changes8, 9, 10, 11, 12 . . .
  10. Seager, R.; Kushnir, Y.; Herweijer, C.; Naik, N.; Velez, J. Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000 J. Clim. 18, 4065-4088 (2005) .
    • . . . Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large influences on land precipitation and the inability of the coupled models to reproduce many observed regional precipitation changes is linked to the lack of the observed, largely natural change patterns in sea surface temperatures in coupled model simulations13 . . .
    • . . . In contrast, precipitation decreases over Africa, southeast Asia, eastern Australia and southern Europe are the primary cause for the drying trend over there, and the long-term trends and decadal tomultidecadal variations in sea surface temperature (SST) are a major driver for many of the precipitation changes8, 9, 10, 11, 12 . . .
  11. Hoerling, M.; Hurrell, J.; Eischeid, J.; Phillips, A. Detection and attribution of twentieth-century northern and southern African rainfall change J. Clim. 19, 3989-4008 (2006) .
    • . . . Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large influences on land precipitation and the inability of the coupled models to reproduce many observed regional precipitation changes is linked to the lack of the observed, largely natural change patterns in sea surface temperatures in coupled model simulations13 . . .
    • . . . In contrast, precipitation decreases over Africa, southeast Asia, eastern Australia and southern Europe are the primary cause for the drying trend over there, and the long-term trends and decadal tomultidecadal variations in sea surface temperature (SST) are a major driver for many of the precipitation changes8, 9, 10, 11, 12 . . .
    • . . . The drying trend since 1950 over the Sahel results mainly from the decreases in summer rainfall from the 1950s to the mid-1980s (ref. 22) that are related to the observed large warming in the South Atlantic Ocean relative to the North Atlantic Ocean and the steady warming over the Indian Ocean8, 11, together with significant contributions from dynamic vegetation feedback23, 24, which is not simulated in the CMIP models . . .
    • . . . Most CMIP3 models produce the opposite warming pattern in the Atlantic Ocean under GHG-induced global warming and thus increasing precipitation over the Sahel in the twenty-first century11, although a few models do produce some drying over the Sahel under a uniform ocean warming25 . . .
  12. Schubert, S. A US CLIVAR project to assess and compare the responses of global climate models to drought-related SST forcing patterns: Overview and results J. Clim. 22, 5251-5272 (2009) .
    • . . . Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large influences on land precipitation and the inability of the coupled models to reproduce many observed regional precipitation changes is linked to the lack of the observed, largely natural change patterns in sea surface temperatures in coupled model simulations13 . . .
    • . . . In contrast, precipitation decreases over Africa, southeast Asia, eastern Australia and southern Europe are the primary cause for the drying trend over there, and the long-term trends and decadal tomultidecadal variations in sea surface temperature (SST) are a major driver for many of the precipitation changes8, 9, 10, 11, 12 . . .
    • . . . They both represent the variations induced by the El Niño-Southern Oscillation (ENSO), as the SST patterns (Fig. 3b,d) resemble the typical ENSO-induced SST anomaly patterns12 and the temporal coefficient is highly correlated (r = 0.87) with an ENSO index (Fig. 3a) . . .
  13. Hoerling, M.; Eischeid, J.; Perlwitz, J. Regional precipitation trends: Distinguishing natural variability from anthropogenic forcing J. Clim. 23, 2131-2145 (2010) .
    • . . . Previous studies8, 9, 10, 11, 12 show that changes in sea surface temperatures have large influences on land precipitation and the inability of the coupled models to reproduce many observed regional precipitation changes is linked to the lack of the observed, largely natural change patterns in sea surface temperatures in coupled model simulations13 . . .
    • . . . The long-term SST trend is part of the global warming; however, many of the observed decadal to multidecadal SST variations are absent in greenhouse-gas- (GHG) and aerosol-forced coupled model simulations13, implying that these SST variations are unforced, natural variations whose phase or timing and spatial patterns may depend on the initial conditions of the models and thus they are generally irreproducible. . . .
  14. Burke, E. J. Understanding the sensitivity of different drought metrics to the drivers of drought under increased atmospheric CO2 J. Hydrometeorol. 12, 1378-1394 (2011) .
    • . . . Different drought indices can result in somewhat different change patterns, especially on small scales14 . . .
  15. Cook, E. R. Asian monsoon failure and megadrought during the last millennium Science 328, 486-489 (2010) .
    • . . . The PDSI is calculated from a water-balance model forced with observed precipitation and temperature and has been widely used in monitoring drought development over the USA, palaeoclimate reconstruction15 and studying aridity changes2, 5, 6, 16 . . .
  16. van der Schrier, G.; Briffa, K. R.; Jones, P. D.; Osborn, T. J. Summer moisture variability across Europe J. Clim. 19, 2818-2834 (2006) .
    • . . . The PDSI is calculated from a water-balance model forced with observed precipitation and temperature and has been widely used in monitoring drought development over the USA, palaeoclimate reconstruction15 and studying aridity changes2, 5, 6, 16 . . .
  17. Dai, A. G.; Qian, T. T.; Trenberth, K. E.; Milliman, J. D. Changes in continental freshwater discharge from 1948 to 2004 J. Clim. 22, 2773-2792 (2009) .
    • . . . These patterns are also broadly comparable to those seen in observed streamflow trends since 1948 in the world’s main river basins1, 17 . . .
    • . . . The MCA is a standard singular value decomposition method17 that is useful for exploring relationships between two separate fields, although physical interpretations of the MCA modes require additional knowledge . . .
  18. Bretherton, C. S.; Smith, C.; Wallace, J. M. An intercomparison of methods for finding coupled patterns in climate data J. Clim. 5, 541-560 (1992) .
    • . . . As SSTs have large influences on land precipitation and drought, here I carried out a maximum covariance analysis18 (MCA) of global fields of SSTs (40° S–60° N) and sc_PDSI_pm (60° S–75° N) from both observations and the CMIP models (also done for SST versus soil moisture for the model data) . . .
  19. Deser, C.; Phillips, A. S.; Hurrell, J. W. Pacific interdecadal climate variability: Linkages between the tropics and the North Pacific during boreal winter since 1900 J. Clim. 17, 3109-3124 (2004) .
    • . . . There are substantial decadal to multidecadal variations in this ENSO mode from observations as noticed previously19, with the recent period since about 1999 becoming cooler in the central and eastern Pacific than the previous period from 1977 to 1998 (Fig. 3a,b) . . .
    • . . . These multidecadal variations are linked to the Interdecadal Pacific Oscillation (IPO; ref. 28), which switched to a warm phase with above-normal SSTs in the tropical Pacific around 1977 and entered a cold phase around 1999 (refs 19, 28; Supplementary Fig . . .
  20. Dai, A.; Wigley, T. M. L. Global patterns of ENSO-induced precipitation Geophys. Res. Lett. 27, 1283-1286 (2000) .
    • . . . The impact of ENSO on drought is reflected by the MCA2 for the sc_PDSI_pm, whose patterns (Fig. 3c,e) largely resemble those of ENSO-induced precipitation20, with drier conditions over Australia, south Asia, northern South America, the Sahel and southern Africa and wetter conditions over the continental USA, Argentina, southern Europe and southwestern Asia in El Niño years. . . .
  21. IPCC Climate Change 2007: The Physical Science Basis. (Cambridge Univ. Press, 2007) , .
    • . . . Figure 4 shows that the first leading MCA modes (MCA1) from observations and the models represent the global warming, as the temporal coefficient is correlated strongly (r = 0.97) with the observed global mean surface temperature (Fig. 4a) and the SST MCA1 patterns (Fig. 4c) resemble the observed warming patterns over the oceans21 . . .
    • . . . The CMIP3 (used for Intergovernmental Panel on Climate Change Fourth Assessment Report; ref. 21) and new CMIP5 model simulations were downloaded from http://cmip-pcmdi.llnl.gov/ . . .
  22. Dai, A.; Lamb, P. J.; Trenberth, K. E.; Hulme, M.; Jones, P. D.; Xie, P. The recent Sahel drought is real Int. J. Climatol. 24, 1323-13331 (2004) .
    • . . . The drying trend since 1950 over the Sahel results mainly from the decreases in summer rainfall from the 1950s to the mid-1980s (ref. 22) that are related to the observed large warming in the South Atlantic Ocean relative to the North Atlantic Ocean and the steady warming over the Indian Ocean8, 11, together with significant contributions from dynamic vegetation feedback23, 24, which is not simulated in the CMIP models . . .
  23. Zeng, N.; Neelin, J. D.; Lau, K. M.; Tucker, C. J. Enhancement of interdecadal climate variability in the Sahel by vegetation interaction Science 286, 1537-1540 (1999) .
    • . . . The drying trend since 1950 over the Sahel results mainly from the decreases in summer rainfall from the 1950s to the mid-1980s (ref. 22) that are related to the observed large warming in the South Atlantic Ocean relative to the North Atlantic Ocean and the steady warming over the Indian Ocean8, 11, together with significant contributions from dynamic vegetation feedback23, 24, which is not simulated in the CMIP models . . .
  24. Wang, G.; Eltahir, E. A. B.; Foley, J. A.; Pollard, D.; Levis, S. Decadal variability of rainfall in the Sahel: results from the coupled GENESIS-IBIS atmosphere-biosphere model Clim. Dynam. 22, 625-637 (2004) .
    • . . . The drying trend since 1950 over the Sahel results mainly from the decreases in summer rainfall from the 1950s to the mid-1980s (ref. 22) that are related to the observed large warming in the South Atlantic Ocean relative to the North Atlantic Ocean and the steady warming over the Indian Ocean8, 11, together with significant contributions from dynamic vegetation feedback23, 24, which is not simulated in the CMIP models . . .
  25. Held, I. M.; Delworth, T. L.; Lu, J.; Findell, K. L.; Knutson, T. R. Simulation of Sahel drought in the 20th and 21st centuries Proc. Natl Acad. Sci. USA 102, 17891-17896 (2005) .
    • . . . Most CMIP3 models produce the opposite warming pattern in the Atlantic Ocean under GHG-induced global warming and thus increasing precipitation over the Sahel in the twenty-first century11, although a few models do produce some drying over the Sahel under a uniform ocean warming25 . . .
  26. Ackerley, D Sensitivity of twentieth-century Sahel rainfall to sulfate aerosol and CO2 forcing J. Clim. 24, 4999-5014 (2011) .
    • . . . S5 shows that the HadGEM2-CC and HadGEM2-ES models from the CMIP5 broadly reproduce the observed rainfall decline over the Sahel from the 1950s to 1980s, although with reduced amplitudes, and sulphate aerosols have been identified as the main contributor for this simulated decline in the HadGEM2 models26 . . .
  27. Cook, K.H.; Vizy, E. K. Coupled model simulations of the West African monsoon system: Twentieth- and twenty-first-century simulations J. Clim. 19, 3681-3703 (2006) .
    • . . . a, Temporal (black line for SST, red line for sc_PDSI_pm, on the left-side ordinate) and b–e, spatial expansion coefficients of the second leading mode from a MCA of 13-point moving-averaged monthly SST from observations27 and sc_PDSI_pm computed from observational forcing (a–c) and from 14 CMIP5 model ensemble-mean simulations (d,e) for 1923–2010 (observational data are unreliable for earlier years) . . .
    • . . . For the twenty-first century, the GHG effect will dominate over the aerosol forcing and thus such aerosol-induced drought over the Sahel may not occur again27 . . .
  28. Dai, A. Link , .
    • . . . These multidecadal variations are linked to the Interdecadal Pacific Oscillation (IPO; ref. 28), which switched to a warm phase with above-normal SSTs in the tropical Pacific around 1977 and entered a cold phase around 1999 (refs 19, 28; Supplementary Fig . . .
    • . . . The IPO has major influences on US precipitation and drought, especially over the southwest USA (ref. 28; Supplementary Fig . . .
  29. Rayner, N. A. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century J. Geophys. Res. 108, 4407 (2003) .
    • . . . I used the Hadley Centre Sea Ice and Sea Surface Temperature data set data set29 in the MCA analysis . . .
  30. Zhao, W. N.; Khalil, M. A. K. The relationship between precipitation and temperature over the contiguous United-States J. Clim. 6, 1232-1236 (1993) .
    • . . . The stippling indicates the trend is statistically significant at the 5% level, with the effective degree of freedom computed using the method of ref. 30 . . .
  31. Brohan, P.; Kennedy, J.J.; Harris, I.; Tett, S.F.B.; Jones, P.D. Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850 J. Geophys. Res. 111, D12106 (2006) .
    • . . . a–f, The blue line (right-side ordinate) is the global mean surface temperature from observations31 in a and global mean surface air temperature from the models in b, which is the temporal coefficient of the MCA1 for the model SST (black) and sc_PDSI_pm (red) . . .
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