No 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.

<p>Recent studies project a significant increase in drought frequency over most continents over the 21<sup>st</sup> century. However, few studies have specifically looked at extreme droughts, defined here as having a return period larger than 20 years. In this work, two large climate model ensembles, the 50-member Canadian Earth System Model (CanESM2) and the 40-member Community Earth System Model (CESM1), both under the RCP8.5 scenario are used to project the evolution of the extreme drought frequency in the near (2036-2065) and far future (2070-2099) relative to the 1980-2009 historical period. The use of a large ensemble allows for a robust estimation of the frequency of very large droughts. Frequency changes for the 2, 20 and 100-year droughts were computed.</p><p>Extreme meteorological droughts were globally assessed using the short-term (1-month) and long-term (24-month) Standardized Precipitation Index (SPI). Extreme hydrological extreme droughts were assessed by the 1-month Streamflow Drought Index (SDI), using a lumped hydrological model on 5797 North American catchments to transform climate model outputs into catchment streamflows.</p><p>Results show that both climate models project increases of extreme meteorological drought frequency over many of the world’s regions, with a typical two or three-fold increase. The spatial distribution of regions with increasing meteorological drought frequency mostly matches those projected changes in future mean annual precipitation. Changes in future extreme hydrological droughts are dramatically more severe than for meteorological droughts, with up to a 27-times increase in frequency for the 100-year hydrological droughts, outlining the large impact of temperature change. The frequency change is the largest for the 100-year compared to the 2 and 20-year hydrological droughts.</p>