The global and regional impacts of climate change under representative concentration pathway forcings and shared socioeconomic pathway socioeconomic scenarios

Abstract

This paper presents an evaluation of the global and regional consequences of climate change for heat extremes, water resources, river and coastal flooding, droughts, agriculture and energy use. It presents change in hazard and resource base under different rates of climate change (representative concentration pathways (RCP)), and socio-economic impacts are estimated for each combination of RCP and shared socioeconomic pathway. Uncertainty in the regional pattern of climate change is characterised by CMIP5 climate model projections. The analysis adopts a novel approach using relationships between level of warming and impact to rapidly estimate impacts under any climate forcing. The projections provided here can be used to inform assessments of the implications of climate change. At the global scale all the consequences of climate change considered here are adverse, with large increases under the highest rates of warming. Under the highest forcing the global average annual chance of a major heatwave increases from 5% now to 97% in 2100, the average proportion of time in drought increases from 7% to 27%, and the average chance of the current 50 year flood increases from 2% to 7%. The socio-economic impacts of these climate changes are determined by socio-economic scenario. There is variability in impact across regions, reflecting variability in projected changes in precipitation and temperature. The range in the estimated impacts can be large, due to uncertainty in future emissions and future socio-economic conditions and scientific uncertainty in how climate changes in response to future emissions. For the temperature-based indicators, the largest source of scientific uncertainty is in the estimated magnitude of equilibrium climate sensitivity, but for the indicators determined by precipitation the largest source is in the estimated spatial and seasonal pattern of changes in precipitation. By 2100, the range across socioeconomic scenario is often greater than the range across the forcing levels.

Impacts of long-term temperature change and variability on electricity investments

“…for RCP 8.5 w/ and w/o temperature impacts, respectively. These values are consistent with cumulative global emission estimates from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) 33 for RCP 8.5 scenarios between 5130 and 7010 Gt CO 2 eq… We note that while the RCP 8.5 scenario may not be reflective of a business-as-usual scenario 34 , it is a well-established scenario explored in detail in the literature 35,36 . We construct these scenarios using a modified version of Global Change Assessment Model (GCAM)-USA, a global multisector human-Earth systems model with state-level details in the U.S. that fully endogenizes the impacts of temperature change on the provision of buildings cooling and heating service demands, subannual (monthly day and night) electricity load profiles, and associated electric capacity and investment requirements (Methods) 37 .…”

Challenges in modeling and predicting floods and droughts: A review

“…Models representing human influences: To study the influence of water management decisions on hydrologic extremes, further development of models representing human influences on the water cycle is needed, especially at a regional scale. When predicting future hydrologic extremes (Arnell et al, 2019), socioeconomic scenarios (Wada et al, 2016), future technological advancements in the water sector (Graham et al, 2018), and regional water use scenarios (Yao, Tramberend, Kabat, Hutjes, & Werners, 2017) need to be considered in addition to climate scenarios as for example, by Winsemius et al (2016) in an assessment of future flood risk.…”

“…Integrating landscape models with flood inundation models in order to better represent the feedbacks between the two, and obtain improved projections of changing flood risk in vulnerable regions of the world with dynamic river systems, remains a major long‐term goal. Models representing human influences : To study the influence of water management decisions on hydrologic extremes, further development of models representing human influences on the water cycle is needed, especially at a regional scale. When predicting future hydrologic extremes (Arnell et al, 2019), socioeconomic scenarios (Wada et al, 2016), future technological advancements in the water sector (Graham et al, 2018), and regional water use scenarios (Yao, Tramberend, Kabat, Hutjes, & Werners, 2017) need to be considered in addition to climate scenarios as for example, by Winsemius et al (2016) in an assessment of future flood risk. Model‐comparison frameworks : Model choice could be facilitated by comparing the suitability of different types of models for flood and drought estimation including statistical, hydrological, and land‐surface models within a model‐comparison framework. For example, van der Wiel et al (2019) compared flood return periods estimated using a parametric distribution within a classical frequency analysis framework to empirical estimates derived using a large ensemble and Winter et al (2020) compared flood risk estimates derived using an event‐based stochastic model with an indirect modeling approach (weather generator and hydrological model). Exploring added value of deep learning : Future modeling efforts should explore the potential benefits of deep learning for streamflow simulation and data assimilation.…”

Regional Differences in Spatiotemporal Drought Characteristics in Great Britain

“…Globally, drought is one of the most damaging natural hazards (Blauhut, 2020). Droughts pose a major threat to lives and livelihoods across the world, and the impacts of drought are expected to increase in future on a global scale, due to anthropogenic warming (Prudhomme et al, 2014) and socioeconomic changes (Arnell et al, 2019). As such, there is a growing demand for drought and water resources management systems that enable policymakers and practitioners to appraise the risk of drought occurring, under historical conditions as well as using future projections, to identify appropriate adaptation options.…”