A method for investigating the relative importance of three components in overall uncertainty of climate projections

Abstract

Climate model response (M) and greenhouse gas emissions (S) uncertainties are consistently estimated as spreads of multi-model and multi-scenario climate change projections. There has been less agreement in estimating internal climate variability (V). In recent years, an initial condition ensemble (ICE) of a climate model has been developed to study V. ICE is simulated by running a climate model using an identical climate forcing but different initial conditions. Inter-member differences of an ICE manifestly represent V. However, ICE has been barely used to investigate relative importance of climate change uncertainties. Accordingly, this study proposes a method of using ICEs, without assuming V as constant, for investigating the relative importance of climate change uncertainties and its temporal-spatial variation. Prior to investigating temporal-spatial variation in China, V estimated using ICE was compared to that using multi-model individual time series at national scale. Results show that V using ICE is qualitatively similar to that using multi-model individual time series for temperature. However, V is not constant for average and extreme precipitations. V and M dominate before 2050s especially for precipitation. S is dominant in the late 21st century especially for temperature. Mean temperature change is projected to be 30-70% greater than its uncertainty until 2050s, while uncertainty becomes 10-40% greater than the change in the late 21st century. Precipitation change uncertainty overwhelms its change by 70-150% throughout 21st century. Cold regions (e.g., northern China and Qinghai-Tibetan Plateau) tend to have greater temperature change uncertainties. In dry regions (e.g., northwest China), all three uncertainties tend to be great for changes in average and extreme precipitations. This study emphasizes the importance of considering climate change uncertainty in impact studies, especially taking into account that V is irreducible in the future. Using ICEs without assuming V as constant is an appropriate approach to study climate change uncertainty.

Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

“…These different uncertainty sources in climate projections have been quantified by multiple studies (Yip et al, 2011;Zhuan et al, 2018;Evin et al, 2019). The relative importance of different uncertainty sources varies depending on factors like the type of climate variable and temporal and spatial scales (Zhuan et al, 2018).…”

“…These different uncertainty sources in climate projections have been quantified by multiple studies (Yip et al, 2011;Zhuan et al, 2018;Evin et al, 2019). The relative importance of different uncertainty sources varies depending on factors like the type of climate variable and temporal and spatial scales (Zhuan et al, 2018). For example, many studies have demonstrated that model uncertainty is generally dominant in rainfall projections rather than scenario uncertainty throughout the 21st century, while scenario uncertainty becomes gradually more important in the late 21st century, particularly for temperature projections (Zhuan et al, 2018;Yip et al, 2011).…”

Assessment of extreme flows and uncertainty under climate change: disentangling the contribution of RCPs, GCMs and internal climate variability

“…These different uncertainty sources in climate projections have been quantified by multiple studies (Yip et al, 2011;Zhuan et al, 2018;Evin et al, 2019). The relative importance varies depending on factors like the type of climate variable and temporal and spatial scales (Zhuan et al, 2018).…”

“…These different uncertainty sources in climate projections have been quantified by multiple studies (Yip et al, 2011;Zhuan et al, 2018;Evin et al, 2019). The relative importance varies depending on factors like the type of climate variable and temporal and spatial scales (Zhuan et al, 2018). For example, many studies have demonstrated that model uncertainty is generally dominant in rainfall projections rather than scenario uncertainty throughout the 21st century, while scenario uncertainty becomes gradually more important in the late 21st century, particularly for temperature projections (Zhuan et al, 2018;Yip et al, 2011).…”

“…The relative importance varies depending on factors like the type of climate variable and temporal and spatial scales (Zhuan et al, 2018). For example, many studies have demonstrated that model uncertainty is generally dominant in rainfall projections rather than scenario uncertainty throughout the 21st century, while scenario uncertainty becomes gradually more important in the late 21st century, particularly for temperature projections (Zhuan et al, 2018;Yip et al, 2011). In the near future, internal climate variability contributes largely to the total uncertainty, especially for rainfall projections, and becomes more important with decreasing temporal and spatial scales (Hingray and Saï d, 2014;Giorgi, 2002).…”