Ranjini Swaminathan1,2, Colin Jones1,3, Robert Parker1,2, Douglas Kelley1,4, Jeremy Walton1,5

1UKESM Core Group, 2National Centre for Earth Observation, 3National Centre for Atmospheric Science, 4Centre for Ecology and Hydrology, 5Met Office Hadley Centre

The sixth Coupled Model Intercomparison Project (CMIP6) Scenario Model Intercomparison Project (ScenarioMIP) coordinates future climate projections, sampling a range of emission scenarios produced by integrated assessment models [O’Neill et al. 2016]. ScenarioMIP simulations are identified by a combination of an underpinning Shared Socioeconomic Pathway (SSP) number, which ranges from 1 for a sustainable future to 5 for a fossil fuel intense future, and the RCP (Representative Concentration Pathway) value, denoting the global mean top of the atmosphere radiation perturbation (in units of Wm-2) for the year 2100 (specifically, 1.9, 4.5, 7.0, 8.5). More detail on the RCPs can be found in Moss et al. 2010 and Taylor et al. 2009. UKESM1 [Sellar et al. 2019] ScenarioMIP simulations for SSPs 1-2.6, 2-4.5, 3-7.0 and 5-8.5 are already available on the Earth System Grid Federation (ESGF) node, comprising at least 5 members for all SSPs and 13 members for SSP3-70 and (shortly) SSP1-26. We analyse when UKESM1 ScenarioMIP ensemble members will exceed key global warming thresholds. We then assess the patterns of regional climate change simulated in UKESM1, centred on these “exceedance dates” to give a snapshot of how the future Earth System might look when we reach these GWTs. We present some early results from this analysis.

We define a Global Warming Threshold (GWT) as a specific value of Global Mean Surface Air Temperature (GSAT), calculated as an anomaly with respect to the GSAT value averaged over the period 1850-1900. To limit the influence of short-term variability, we apply a 21 year centred running mean to the ScenarioMIP GSAT values from each UKESM1 ensemble member. A GWT exceedance year is calculated for individual ensemble members (as well as the ensemble mean) as the year at which the 21 year mean GSAT anomaly exceeds a given GWT (e.g. a GSAT value 3°C warmer than 1850-1900 mean – see Figure 1). We use GWTs and years of exceedance to help answer questions such as (a) What is the regional pattern and magnitude of climate change at different levels of global mean warming? (b) What regional changes and associated impacts can we avoid if we restrict warming to a given GWT compared to warmer values? Our initial focus is on changes at specific GWTs in several key climate variables such as surface temperature, precipitation and soil moisture that are of particular importance for human activities.

Figure 1: Global Warming Threshold (GWT) exceedance year computation: For each UKESM1 historical member and subsequent SSP, a 21 year centred running mean GSAT anomaly is calculated with respect to 1850-1900 mean GSAT. The first year this anomaly exceeds a given threshold temperature value for a given ensemble member (or ensemble mean) is taken as the year of exceedance for that ensemble member (or ensemble mean). Calculation of the exceedance year for a 3°C GWT is shown in the figure.

First analysis - Figure 2First analysis - Figure 2

Figure 2. Zonal mean surface temperature anomalies shown for the European winter (DJF) and summer (JJA) seasons under SSP3-7.0 and for different warming thresholds.

The zonal mean surface temperature anomalies in Fig. 2 highlight the significant Arctic amplification we can expect to see in the boreal winters of the future. While there is amplification at both poles, the magnitude of increase in the Arctic is much higher and also increases with increasing warming thresholds. One important reason for this is sea ice loss in the summer months – Arctic sea ice melts in the summer, increasing the area of open ocean exposed to the atmosphere [Dai et al. 2019]. Regions with extensive sea ice or snow cover have high values of surface albedo and reflect away most of the sun’s radiation, keeping temperatures low. When sea-ice melts, this cover is lost and the exposed ocean with a lower albedo, absorbs significantly more solar radiation, warming the surface further. As the climate warms to higher temperature thresholds, increasing loss of Arctic sea-ice drives a substantial amplification of winter warming, in excess of 25°C in the central Arctic for a GWT of 5°C. Arctic amplification will begin to decrease once the majority of sea ice has melted, and the strength of the sea-ice albedo feedback decreases. However, amplified Arctic warming may still occur, mainly as a result of increased water vapour absorption of terrestrial radiation.

As the primary goal of the 2015 Paris Climate Agreement was to limit the increase in global mean surface temperature to well below 2°C above pre-industrial levels [Rhodes, C.J 2016], we explore the state of the Earth System at GWT = 2°C in greater detail (Figures 3 and 4). We plot land temperatures only to help focus attention onto changes that impact human activities on land.

First analysis - Figure 2

Figure 3: Seasonal mean surface temperature anomaly averaged across the UKESM1 SSP3-70 ensemble and centred on the year of exceedance at GWT = 2°C. Anomalies are calculated with respect to 1850-1900 mean and shown for the northern hemisphere winter (DJF) and summer (JJA) seasons.

The primary signal is a strong amplification of winter warming across the high latitude Northern Hemisphere, with most areas north of 60°N warming in excess of 4°C. Over central Europe and North America summer warming exceeding 3°C, and summer precipitation decreasing by 20-30% will lead to major impacts on human activities in these two critical agricultural regions. Meanwhile, local warming in the Amazon is amplified relative to the global mean value by 50-75% which, combined with projected drying of the wet season (~30% in DJF) may have negative impacts on the long-term health in a rainforest already experiencing savannafication and deforestation.

Conversely, looking at areas with projected increases in precipitation, we see a significant increase in monsoon rainfall over the Indian subcontinent, likely explaining the relatively smaller seasonal increase in temperature across this region. While increased monsoon rains can be favourable for agriculture, such increases may also be associated with significant flood risk.

Figure 4: Projected seasonal changes in precipitation at 2oC global mean warming under SSP3-7.0. Changes are shown as a percentage change relative to the 1850-1900 period and for northern hemisphere winter (DJF) and summer (JJA) seasons. Values below 0.5mm/day in the 1850-1900 period are greyed out.

Figure 5: Absolute change in seasonal surface temperature anomalies between 2°C and 4°C mean warming under SSP3-7.0 for norther hemisphere winter (DJF) and summer (JJA) seasons.

In order to motivate the scale of mitigation required to stay below 2°C global warming, it is useful to consider the scale of regional changes seen across different global warming thresholds. In Figure 5, for example, we plot the increase in seasonal mean surface temperatures as mean global warming increases from 2°C to 4°C and see that some of the earlier patterns of regional change continue to be visible. In particular, the high Northern latitudes continue to experience an amplified warming rate, as does the Amazon region.

A similar analysis extended to a broader range of climate variables will provide a more complete picture of the benefits of restricting global warming to 2°C. We will also expand our analysis to include other models from CMIP6, focusing on sensitive regions such as the Mediterranean, Amazon and studying the co-variability of changes across different components of the coupled Earth System.

References

  • O’Neill, Brian C., Claudia Tebaldi, Detlef P. Van Vuuren, Veronika Eyring, Pierre Friedlingstein, George Hurtt, Reto Knutti et al. “The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6.” (2016).
  • Moss, Richard H., Jae A. Edmonds, Kathy A. Hibbard, Martin R. Manning, Steven K. Rose, Detlef P. Van Vuuren, Timothy R. Carter et al. “The Next Generation of Scenarios for Climate Change Research and Assessment.” Nature 463, no. 7282 (2010): 747-756.
  • Taylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl. “An Overview of CMIP5 and the Experiment Design.” Bulletin of the American Meteorological Society 93.4 (2012): 485-498.
  • Sellar, Alistair A., Colin G. Jones, Jane P. Mulcahy, Yongming Tang, Andrew Yool, Andy Wiltshire, Fiona M. O’connor et al. “UKESM1: Description and Evaluation of the UK Earth System Model.” Journal of Advances in Modeling Earth Systems 11, no. 12 (2019): 4513-4558.
  • Dai, Aiguo, Dehai Luo, Mirong Song, and Jiping Liu. “Arctic Amplification is Caused by Sea-ice Loss Under Increasing CO 2.” Nature communications 10, no. 1 (2019): 1-13.
  • Rhodes, Christopher J. “The 2015 Paris climate change conference: COP21.” Science progress 99.1 (2016): 97-104.
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