Projection of Droughts in Amu Darya River Basin for Shared Socioeconomic Pathways

Droughts significantly affect socioeconomic and the environment primarily by decreasing the water availability of a region. This study aims to assess the changes in drought characteristics in Central Asia's transboundary Amu Darya river basin for four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The precipitation, maximum and minimum temperature (Pr, Tmx and Tmn) simulations of 19 global climate models (GCMs) of the coupled model intercomparison project phase 6 (CMIP6) were used to select the best models to prepare the multimodel ensemble (MME). The standard precipitation evapotranspiration index (SPEI) was used to estimate droughts for multiple timescales from Pr and potential evapotranspiration (PET) derived from Tmx and Tmn. The changes in the frequency and spatial distribution of droughts for different severities and timescales were evaluated for the two future periods, 2020–2059 and 2060-2099, compared to the base period of 1975-2014. The study revealed four GCMs, AWI-CM-1-1-MR, CMCC-ESM2, INM-CM4-8 and MPI-ESM1-2-LR, as most suitable for projections of droughts in the study area. The multimodel ensemble (MME) mean of the selected GCMs showed a decrease in Pr by -3 to 12% in the near future and a change in the range of 3 to -9% in the far future in most parts of the basin for different SSPs. The PET showed almost no change in most parts of the basin in the near future and an increase in the range of 10 to 70% in the far future. The change (%) in projected drought occurrence showed to noticeably decrease in the near future, particularly for moderate droughts by up to ≤-50% for SSP5-8.5 and an increase in the far future by up to ≥30% for SSP3-7.0. The increase in all severities of droughts was projected mostly in the center and northwest of the basin. Overall, the results showed a drought shift from the east to the northwest of the basin in the future.

Future Projections and Uncertainty Assessment of Precipitation Extremes in Iran from the CMIP6 Ensemble

Scientists who want to know future climate can use multimodel ensemble (MME) methods that combine projections from individual simulation models. To predict the future changes of extreme rainfall in Iran, we examined the observations and 24 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over the Middle East. We applied generalized extreme value (GEV) distribution to series of annual maximum daily precipitation (AMP1) data obtained from both of models and the observations. We also employed multivariate bias-correction under three shared socioeconomic pathway (SSP) scenarios (namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We used a model averaging method that takes both performance and independence of model into account, which is called PI-weighting. Return levels for 20 and 50 years, as well as the return periods of the AMP1 relative to the reference years (1971–2014), were estimated for three future periods. These are period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From this study, we predict that over Iran the relative increases of 20-year return level of the AMP1 in the spatial median from the past observations to the year 2100 will be approximately 15.6% in the SSP2-4.5, 23.2% in the SSP3-7.0, and 28.7% in the SSP5-8.5 scenarios, respectively. We also realized that a 1-in-20 year (or 1-in-50 year) AMP1 observed in the reference years in Iran will likely become a 1-in-12 (1-in-26) year, a 1-in-10 (1-in-22) year, and a 1-in-9 (1-in-20) year event by 2100 under the SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios, respectively. We project that heavy rainfall will be more prominent in the western and southwestern parts of Iran.

Projected drought conditions by CMIP6 multimodel ensemble over Southeast Asia

Southeast Asia (SEA) is vulnerable to climate extremes due to its large and growing population, long coastlines with low-lying areas, reliance on agricultural sector developments. Here, the latest Coupled Model Intercomparison Project Phase 6 (CMIP6) was employed to examine future climate change and drought in this region under two SSP–RCP (shared socioeconomic pathway–representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The CMIP6 multimodel ensemble mean projects a warming (wetting) of 1.99–4.29 °C (9.62–18.43%) in the 21st century. The Standardized Precipitation Evapotranspiration Index at 12-month time scales (SPEI-12) displays moderate-to-severe dry conditions over all countries during the near-future period, then the wet condition is projected from mid-future to far-future periods. The projected drought characteristics show relatively longer durations, higher peak intensities, and more severities under SSP5-8.5, while the higher number of events are projected under SSP2-4.5. Overall, the SPEI-12 over SEA displays significant regional differences with decreasing dryness trend toward the 21st century. All these findings have important implications for policy intervention to water resource management under a changing climate over SEA.

Atlantic Niño/Niña Prediction Skills in NMME Models

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

An evaluation of CMIP5 precipitation simulations using ground observations over ten river basins in China

Using the precipitation measurements obtained from 2,419 ground meteorological stations over China from 1960 to 2005 as benchmark, the performance of 21 single-mode precipitation data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) were evaluated using Taylor diagrams and several statistical metrics. Based on statistical metrics, the models were ranked in terms of their ability to reproduce similar patterns in precipitation relative to the observations. Except in Southeast and Pearl river basins, research results show that all model ensemble means overestimate in the rest of the river basins, especially in Southwest and Northwest. The performance of CMIP5 models is quite different among each river basin; most models show significant overestimation in Northwest and Yellow and significant underestimations in Southeast and Pearl. The simulations are more reliable in Songhua, Liao, Yangtze, and Pearl than in other river basins according to spatial distribution and interannual variability. No individual model performs well in all the river basins both spatially and temporally. In Songhua, Liao, Yangtze, and Pearl, precipitation indices are more consistent with observations, and the spread among models is smaller. The multimodel ensemble selected from the most reasonable models indicates improved performance relative to all model ensembles.