The early 20th century warming: Anomalies, causes, and consequences

Abstract

The most pronounced warming in the historical global climate record prior to the recent warming occurred over the first half of the 20th century and is known as the Early Twentieth Century Warming (ETCW). Understanding this period and the subsequent slowdown of warming is key to disentangling the relationship between decadal variability and the response to human influences in the present and future climate. This review discusses the observed changes during the ETCW and hypotheses for the underlying causes and mechanisms. Attribution studies estimate that about a half (40-54%; p > .8) of the global warming from 1901 to 1950 was forced by a combination of increasing greenhouse gases and natural forcing, offset to some extent by aerosols. Natural variability also made a large contribution, particularly to regional anomalies like the Arctic warming in the 1920s and 1930s. The ETCW period also encompassed exceptional events, several of which are touched upon: Indian monsoon failures during the turn of the century, the "Dust Bowl" droughts and extreme heat waves in North America in the 1930s, the World War II period drought in Australia between 1937 and 1945; and the European droughts and heat waves of the late 1940s and early 1950s. Understanding the mechanisms involved in these events, and their links to large scale forcing is an important test for our understanding of modern climate change and for predicting impacts of future change. This article is categorized under:Paleoclimates and Current Trends > Modern Climate Change.

Keywords: attributing causes to climate change; decadal climate variability; extreme weather and climate events; observed climate change.

Figure 1

Mean surface temperature over (a) land, (b) ocean (sea surface temperatures), and (c) global (combined land + ocean) in observations (mean: black; gray lines: possible realizations given uncertainty [Kennedy et al., 2011; Morice et al., 2012]), CMIP5 multimodel mean simulations with all historical forcings (thick red line: average and thin lines: individual simulations) relative to an average over the full period. All model data are masked to observational coverage, and for combined land and sea a blend of surface air temperatures and sea surface temperature is calculated following Cowtan and Way (2014). (d) Residual variability for land and sea blend after subtracting the multimodel mean forced component, compared to 5–95% uncertainty ranges of multimodel control simulations (Method and models used the same as in Schurer et al., 2018). The blue line in panel (c) shows a global proxy‐based reconstruction (Crowley, Obrochta, & Liu, 2014) and the pink bar highlights the ETCW

Figure 2

Spatial maps showing decadal temperature trends for boreal cold (a, November–March) and warm season (b, April–October) for the early 20th century (1901–1940) in HadCRUT4 (Morice et al., 2012). (c, d) Spatial temperature anomalies from HadCRUT4 after the multimodel mean CMIP5 response to all historical forcings combined has been subtracted based on analysis discussed below. All panels are masked where coverage is too sparse (gray); in panels (a) and (b) where coverage in either the first or second half of the period drops below 30%; in panels (c) and (d) where coverage in any season drops below 30% in either the first or second half of the decade. For details see Supporting information

Figure 3

Anomalies in Arctic Sea ice extent (million km²) from Walsh et al. (2017) in the months of April through to August relative to the climatology from 1931 to 1960, smoothed with a 10‐year moving average

Figure 4

(Top) Annual mean time series of climate forcings agents: CO₂ concentrations (from reconstructions [Yoshimori, Stocker, Raible, & Renold, 2005] and Mauna Loa measurements, NOAA), total solar irradiance (TSI; Coddington, Lean, Pilewskie, Snow, & Lindholm, 2016); note that effective solar forcing of 1 W/m² in TSI translates to ca. 0.175 W/m² when correcting for insulated area and albedo, which is accounted for by using a different scale), as well as estimated forcing from stratospheric and tropospheric aerosols (from NASA/GISS CMIP5). (Bottom) Indices of the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). The AMO index is defined (Trenberth & Shea, 2006) as the difference in SST averages over the regions (0–60°N, 0–80°W) and (60°S–60°N). The PDO is calculated by projecting the first Empirical Orthogonal Function mode onto the median realization of HadSST3.1.1.0 at 5° resolution (from the Climate Explorer, following Mantua, Hare, Zhang, Wallace, and Francis (1997)

Figure 5

Top panel: Scaling factors, that is, magnitude of model fingerprint consistent with observations for the response to greenhouse gases (red), other anthropogenic factors (blue) and natural factors (solar and volcanic, green) from an analysis of decadal temperatures over 1862–2012. This translates into an estimated contribution to warming 1901–1950 by increases in greenhouse gases, natural forcings, and other anthropogenic forcings compared to the ensemble of HadCRUT observed warming accounting for uncertainty (gray histogram) shown in the middle panel. Dots: Multimodel unscaled best estimate of contribution by forcing. pdfs: Estimated contribution from attribution analysis allowing for up‐ or down‐scaling as long as consistent with observations. Dashed line: Trend from a proxy reconstruction (Crowley et al., 2014). Bottom panel: Residual warming not explained by forcing (purple pdf) compared to CMIP5 control run trends over 50 years to estimate internal variability (green). All analysis is done with model data masked to replicate the data coverage of observed data, and merging sea surface temperature with air temperature over land (Cowtan et al., 2015), solid lines show results using an informative prior, dashed lines an uninformative prior. After Schurer et al., 2018

Figure 6

Top panel: Hovmoeller diagram of standardized zonal mean temperature anomalies over land and ocean (top), land only (middle) and sea surface temperature (bottom) after the multimodel mean forced signal, represented by the average of model simulations forced by historical forcing has been subtracted. The diagram is in standard deviation units of zonal mean variability in the data‐covered parts of each zone relative to multimodel interannual variability from control simulations. Data are masked when data coverage for a particular zonal band drops below 10% (shown in gray; see Figure S6 for a version restricted to >30% coverage)

Figure 7

Annual time series of circulation indices, expressed as anomalies from 1961 to 1990. Shown are series from two reanalyses (20CRv2c, ERA20C), the atmospheric model simulations ERA20CM (10 members; ensemble mean and spread are indicated (Hersbach et al., 2015) and series based on observations or reconstructions. Shown are the seasonal mean strength of the boreal Hadley cell (the maximum of the meridional mass stream function), its latitudinal position (latitude of the maximum), the Pacific Walker cell (difference in 500 hPa ω between the areas [10°S–10°N, 180–100°W] and [10°S–10°N, 100–150°E]), the North Atlantic oscillation (NAO) and the Arctic dipole mode (here defined as the SLP difference north of 60°N between the western and the eastern hemisphere; after Brönnimann et al. (2009) and Brönnimann et al. (2009). Additionally, a statistical reconstruction of the Pacific Walker cell is shown (Brönnimann, Stickler, Griesser, Ewen, et al., 2009; Brönnimann, Stickler, Griesser, Fischer, et al., 2009) and an observation‐based NAO index based on HadSLP2p data (Allan & Ansell, 2006). All series are for December–February except Hadley cell position (January–December) and Indian summer monsoon rainfall (June–August, Sontakke et al., 2008). Colored numbers indicate correlations between the correspondingly colored reanalyses or observations with the ERA20CM ensemble mean (* and ** denote 95 and 99% significance). The purple line for Indian summer monsoon rainfall is based on a reconstruction (Zhou et al., 2010). Severe and phenomenal droughts (Wang, 2006) over India are added as circles

Figure 8

Top panel: Time‐height cross section of seasonal‐mean temperature anomalies as a function of pressure and year for the European Arctic in winter. Middle panel: Sulfate concentrations as a function of time from the Lomonosovfonna ice core (Svalbard). Bottom panel: Reconstructed 850 hPa geopotential height anomalies (relative to 1961–1990) for annual means in (left) 1912–1918, (middle) 1919–1929, and (right) 1930–1939 (from Grant et al., 2009)

Figure 9

Record heat waves in the Midwestern United States and the role of spring drought: (a) time evolution of maximum of daily maximum (TXx) and minimum (TNx) temperature averaged over the Great Plains; (b, c) summer heat wave frequency (HWF [days per summer]), and (d, e) heat wave amplitude (HWA [°C]) averaged over the (left) 1930s and (right) 1950s decade; (f) the summer HWA over the Great Plains, following the 10 driest (orange, average red) and wettest springs (light blue, average dark blue) respectively, calculated from two separate drought indices (standardized precipitation index [SPI], and palmer drought severity index [PDSI]), taken from all springs and summers over 1920–2012 and compared to a 5–95% range for an average of 1000 sets of 10 randomly sampled springs. The heat wave amplitude in summers following the driest springs is significantly larger than those following random springs. TXx and TNx values are taken from HadEX2 (1901–2010) and GHCNDEX (1951–2016). (Based on data from Cowan et al. (2017))

Figure 10

Schematic figure of six climatic anomalies during the ETCW. SST anomalies from HadSST1.1, given as annual mean over indicated period relative to the average of the preceding 15 and following 15 years. Mechanisms discussed in the text are shown schematically