Overview of the approach

The assessment involves the application of a suite of spatially-explicit impacts models run with scenarios describing a range of emissions and socio-economic futures. These emissions and socio-economic futures are here represented by the A1b, A2, B1 and B2 SRES storylines (IPCC Intergovernmental Panel on Climate Change 2000). Scenarios characterising the spatial and seasonal distribution of changes in climate and sea level around 2020, 2050 and 2080 are constructed from up to 21 global climate models (Meehl et al. 2007a) in order to assess the climate-driven uncertainty in the projected impacts for a given future. The period 1961-1990 is used as the climate baseline.

The impact sectors and indicators are summarised in Table 1 (see Supplementary Information for details of the impact models). They span a range of the biophysical and socio-economic impacts of climate change, but do not represent a fully comprehensive set covering all impact areas which may be of interest; they represent an ‘ensemble of opportunity’ based on the availability of models. All the land-based impact models use the same baseline climatology, and all the indicators relating to socio-economic conditions use the same socio-economic data. The impact assessment is therefore harmonised, but is not a fully integrated assessment because interactions between sectors are not represented. Only one impact model is used in each sector, so the uncertainty associated with impact model structure and form is not considered.

Table 1 Summary of the impact indicators Full size table

The socio-economic impacts of climate change in a given year are expressed relative to the situation in that year in the absence of climate change (i.e. assuming that the climate remains the same as over the baseline period 1961-1990). For the ‘pure’ biophysical indicators—crop productivity, suitability of land for cropping, terrestrial ecosystems and soil organic carbon—impacts are compared with the 1961-1990 baseline. Impacts are presented at the regional scale (Supplementary Table 1).

Most of the indicators represent change in some measurable impact of climate change, such as the average annual number of people flooded in coastal floods or crop productivity. Three of the indicators (water scarcity, river flooding and crop suitability), however, represent change in exposure to impact. The extent to which exposure translates into impact depends on the water management and agricultural practices in place, but these are so locally diverse and dependent on local context that it is currently not feasible to represent them numerically in global-scale impacts models. The indicators do not incorporate the effects of adaptation to climate change, with the exception of crop productivity where the crop variety planted varies with climate (see Supplementary Information).

Impacts can be expressed in either absolute or relative terms, and there are advantages and disadvantages in both when comparing impacts across regions. Large percentage impacts in a region may represent small absolute numbers and therefore make a small contribution to the global impact, but may indicate substantial impacts in the region itself. In contrast, a small percentage impact in another region may produce large absolute impact—and thus contribute substantially to the global total—but the implications for the region itself may be smaller. Most of the impacts in this paper are expressed in absolute terms, but relative changes can be calculated from the data in the tables.

The distribution of impacts between regions and across sectors varies with different spatial patterns of change in climate, as represented by different climate models. One possible way of summarising the global and regional impacts of climate change would be to show the ensemble mean (or median) impact for a given sector and region across all climate model patterns, perhaps with some representation of uncertainty through identifying consistency between the different models (as is often done for climatic indicators such as temperature and precipitation). However, this is problematic when the concern is with multiple indicators of impact and comparisons between regions for two main reasons. The calculation of an ensemble mean makes assumptions about the relative plausibility of different climate models, but more importantly the ensemble mean impact does not necessarily represent a plausible future world. Calculating the average reduces the variability between regions and the relationships between sectors and indicators.

An alternative approach is therefore to treat each climate model as the basis for a separate narrative or story, describing a plausible future world with its associations between indicators and regions. Uncertainty in potential impacts is then characterised for each region and indicator by comparing the range in impacts across different climate models, but recognising that aggregated uncertainty—across regions or indicators—is not equivalent to the sum of the individual uncertainty ranges.

Climate and sea level rise scenarios

Climate scenarios were constructed (Osborn et al. 2014) by pattern-scaling output from 21 of the climate models in the CMIP3 set (Meehl et al. 2007a: Supplementary Table 2) to match the changes in global mean temperature projected under the four SRES emissions scenarios A1b, A2, B1 and B2. These global temperature changes were estimated using the MAGICC4.2 simple climate model with parameters appropriate to each climate model (Meehl et al. 2007b: Supplementary Fig. 1a). Pattern-scaling was used rather than simply constructing climate scenarios directly from climate model output partly to better separate out the effects of underlying climate change and internal climatic variability, and partly to allow scenarios to be constructed for all combinations of climate model and emissions scenario. Rescaled changes in mean monthly climate variables (and year to year variability in monthly precipitation) were applied to the CRU TS3.0 0.5×0.5o 1961-1990 climatology (Harris et al. 2014) using the delta method to create perturbed 30-year time series representing conditions around 2020, 2050 and 2080 (Osborn et al. 2014). The terrestrial ecosystem and soil carbon impact models require transient climate scenarios, and these were produced by repeating the CRU 1961-1990 time series and rescaling to construct time series from 1991 to 2100 using gradually increasing global mean temperatures (Osborn et al. 2014). Pattern-scaling makes assumptions about the relationship between rate of forcing and the spatial pattern of change, which have been demonstrated to be broadly appropriate for the averaged climate indicators used here (e.g. Tebaldi and Arblaster 2014), but which do constitute caveats to the quantitative interpretation of results.

Sea level rise scenarios were constructed for 17 climate models. Spatial patterns of change in sea level due to thermal expansion were available for 11 of the models, and for the other six globally-uniform thermal expansion scenarios were calculated using MAGICC4.2. Uniform projections of the contributions of ice melt were added to these patterns, and the patterns were rescaled to correspond to specific global temperature changes using the same methods as applied in Meehl et al. (2007b). Ice melt contributions from Greenland and Antarctica, as well as ice caps and glaciers were calculated following the methodology of Meehl et al. (2007b), with additional data to calculate ice sheet dynamics from Gregory and Huybrechts (2006) (see Brown et al. 2013). Global average sea level rise scenarios are shown in Supplementary Fig. 1b; note that the highest change is produced by one model which is considerably higher—by around 20 cm in 2100—than the others. The effects of changes in the Greenland and Antarctic ice sheet dynamics are not incorporated, but the range in sea level rise across the models is large compared with the potential magnitude of the dynamic effect.

Socio-economic scenarios

Future population and gross domestic product at a spatial resolution of 0.5×0.5o were taken from the IMAGE v2.3 representation of the SRES storylines (van Vuuren et al. 2007). The population living in inland river floodplains was estimated by combining high resolution gridded population data for 2000 (Center for International Earth Science Information Network CIESIN 2004) with flood-prone areas defined in the UN PREVIEW Global Risk Data Platform to estimate the proportions of grid cell population currently living in flood-prone areas. Cropland extent was taken from Ramankutty et al. (2008). It is assumed that river floodplain extent, cropland extent and the proportion of grid cell population living in floodplains do not change over time.