This paper provides an updated analysis of observed changes in extreme precipitation using high-quality station data up to 2018. We examine changes in extreme precipitation represented by annual ...maxima of 1-day (Rx1day) and 5-day (Rx5day) precipitation accumulations at different spatial scales and attempt to address whether the signal in extreme precipitation has strengthened with several years of additional observations. Extreme precipitation has increased at about two-thirds of stations and the percentage of stations with significantly increasing trends is significantly larger than that can be expected by chance for the globe, continents including Asia, Europe, and North America, and regions including central North America, eastern North America, northern Central America, northern Europe, the Russian Far East, eastern central Asia, and East Asia. The percentage of stations with significantly decreasing trends is not different from that expected by chance. Fitting extreme precipitation to generalized extreme value distributions with global mean surface temperature (GMST) as a covariate reaffirms the statistically significant connections between extreme precipitation and temperature. The global median sensitivity, percentage change in extreme precipitation per 1 K increase in GMST is 6.6% (5.1% to 8.2%; 5%–95% confidence interval) for Rx1day and is slightly smaller at 5.7% (5.0% to 8.0%) for Rx5day. The comparison of results based on observations ending in 2018 with those from data ending in 2000–09 shows a consistent median rate of increase, but a larger percentage of stations with statistically significant increasing trends, indicating an increase in the detectability of extreme precipitation intensification, likely due to the use of longer records.
In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and ...reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.
Key Points
We conduct a comprehensive review of precipitation data sets
We evaluate the differences between data sets at different spatial and temporal scales
We explore the opportunities and challenges in generating reliable precipitation estimates
We developed an updated nonstationary bias‐correction method for a monthly global climate model of temperature and precipitation. The proposed method combines two widely used quantile mapping ...bias‐correction methods to eliminate potential illogical values of the variable. Instead of empirical parameter estimation in the more‐common quantile mapping method, our study compared bias‐correction performance when parametric or nonparametric procedures were used to estimate the probability distribution. The results showed our proposed bias‐correction method to be very effective in reducing the model bias: it removed over 80% and 83% of model bias for surface air temperature and precipitation, respectively, during the validation period. Compared with a widely used method of bias correction (delta change), our proposed technique demonstrates improved correction of the distribution of variables. In addition, nonparametric estimation procedures further reduced the mean absolute errors in temperature and precipitation during the validation period by approximately 2% and 0.4%, respectively, compared with parametric procedures. The proposed method can remove over 40% and 60% of the uncertainty from model temperature and precipitation projections, respectively, at the global land scale.
Key Points
The proposed bias‐correction method removed over 80% and 83% of model bias for temperature and precipitation, respectively
The nonparametric estimation procedures further reduced the MAE in GCM simulation compared with parametric procedures
The proposed method can remove over 40% and 60% of the uncertainty from model temperature and precipitation projections, respectively
The effects of heat stress are spatially heterogeneous owing to local variations in climate response, population density, and social conditions. Using global climate and impact models from the ...Inter-Sectoral Impact Model Intercomparison Project, our analysis shows that the frequency and intensity of heat events increase, especially in tropical regions (geographic perspective) and developing countries (national perspective), even with global warming held to the 1.5 °C target. An additional 0.5 °C increase to the 2 °C warming target leads to >15% of global land area becoming exposed to levels of heat stress that affect human health; almost all countries in Europe will be subject to increased fire danger, with the duration of the fire season lasting 3.3 days longer; 106 countries are projected to experience an increase in the wheat production-damage index. Globally, about 38%, 50%, 46%, 36%, and 48% of the increases in exposure to health threats, wildfire, crop heat stress for soybeans, wheat, and maize could be avoided by constraining global warming to 1.5 °C rather than 2 °C. With high emissions, these impacts will continue to intensify over time, extending to almost all countries by the end of the 21st century: >95% of countries will face exposure to health-related heat stress, with India and Brazil ranked highest for integrated heat-stress exposure. The magnitude of the changes in fire season length and wildfire frequency are projected to increase substantially over 74% global land, with particularly strong effects in the United States, Canada, Brazil, China, Australia, and Russia. Our study should help facilitate climate policies that account for international variations in the heat-related threats posed by climate change.
•We assessed the influences of 1.5 °C and 2.0 °C warmings on the multiple sectors.•0.5 °C less warming would help avoid ~38% of the health-related heat exposure.•0.5 °C less warming would help avoid ~50% of the exposure to wildfire.•0.5 °C less warming would help avoid ~48% of the maize-related heat exposure.•Developing countries are likely to experience greater exposure to heat extremes.
This study assesses the simulations of the daily mean, maximum, and minimum temperatures and daily precipitation over China during the period 1990–1999, based on phase 3 and phase 5 of the Coupled ...Model Intercomparison Project (CMIP3 and CMIP5). Fourteen CMIP3 models and 14 CMIP5 models were investigated over eight regions across China. Skill scores quantifying the match between the simulated and observed probability density functions (PDFs) were applied to evaluate the performance of the models. For daily mean, maximum, and minimum temperatures, the results revealed that CMIP3 and CMIP5 models captured the basic pattern of the observed PDFs in all regions. However, the probabilities at lower values were overestimated in most models. In all regions except the west of Northwest China (region 7), all CMIP5 models captured more than 80% of the observed PDFs. Compared with performance at the annual time scale, the models tended to perform relatively worse over the period June to August. The performances of the CMIP5 and CMIP3 models were not as good for daily precipitation as for daily temperature, and the skill scores for precipitation were generally lower than 0.7 in all regions. The amount of drizzle (daily precipitation < 5 mm) was overestimated notably in all regions. The amount of very heavy precipitation (daily precipitation ≥ 20 mm) tended to be underestimated in humid regions but overestimated in arid regions. Compared with CMIP3, CMIP5 models showed some improvements in the simulation of daily mean, maximum, and minimum temperatures, but there was a lack of apparent improvement for simulation of daily precipitation.
Key Points
Most of the GCMs overestimate the probabilities of the lower value of variables
GCMs' performances in daily precipitation are not as good as daily temperature
CMIP5 improves the simulation of daily temperature when compared with CMIP3
We report on the characteristics of precipitation associated with three types of landfalling atmospheric rivers (ARs) over western North America in the winter season from 1980 to 2004. The ARs are ...classified according to three landfalling regions as southern, middle and northern types. Two main centers of precipitation are associated with the contributions by the ARs: one over Baja California linked to the southern type of the ARs, and the other over Washington State correlated with the northern and middle types of the ARs. ARs are seen to play a dominant role in the occurrences of extreme precipitation events, with a proportionately greater impact on more extreme events. Moisture flux convergence makes the dominant contribution to precipitation when ARs and extreme precipitation occur simultaneously in the studied areas. Moisture flux convergence in these cases is, in turn, dominated by the mean and transient moisture transported by the transient wind, with greater contribution from the latter, which is mainly concentrated in certain areas. The magnitude and direction of vertically integrated vapor transport (IVT) also play a role in determining the amount of precipitation received in the three regions considered. Larger IVT magnitude corresponds to more precipitation, while an IVT direction of about 220° (0° indicating east wind) is most favorable for high precipitation amount, which is especially obvious for the northern type of the ARs.
This study focuses on changing trends in precipitation across mainland China during the period 1957-2014. We explore the influence of the El Niño-Southern Oscillation (ENSO), the Pacific Decadal ...Oscillation (PDO), and related large-scale atmospheric circulation variables on the changes in precipitation. The number of wet days showed statistically significant downward trends in North China, Jianghuai, South China, and Southwest of China, but upward trends on the Tibetan Plateau and in Northwest China. However, the number of very wet days increased in Jianghuai, South China and regions in Southwest China, and there was an increase in the spatial variability of a number of rainfall extremes over China. Because the changes in the frequency of wet days and very wet days were non-uniform, an increasing percentage of the total annual precipitation was derived from extreme events over large regions of mainland China. The ENSO and the PDO had a zonal influence on precipitation variability through the modulation of large-scale atmospheric circulation. Both the number of wet days and the frequency of extreme precipitation increased in southern Jianghuai and South China in El Niño years compared with La Niña years. A decrease (increase) in the number of wet days was observed in northern China (southeastern China) during positive PDO-phase years, which was likely a response to the large decrease in Southerly winds.
This study conducts a detection and attribution analysis of the observed changes in extreme precipitation during 1951–2015. Observed and CMIP6 multimodel simulated changes in annual maximum daily and ...consecutive 5‐day precipitation are compared using an optimal fingerprinting technique for different spatial scales from global land, Northern Hemisphere extratropics, tropics, three continental regions (North America and western and eastern Eurasia), and global “dry” and “wet” land areas (as defined by their average extreme precipitation intensities). Results indicate that anthropogenic greenhouse gas influence is robustly detected in the observed intensification of extreme precipitation over the global land and most of the subregions considered, all with clear separation from natural and anthropogenic aerosol forcings. Also, the human‐induced greenhouse gas increases are found to be a dominant contributor to the observed increase in extreme precipitation intensity, which largely follows the increased moisture availability under global warming.
Plain Language Summary
Human influences have been identified in the observed intensification of extreme precipitation at global and continental scales, but quantifying the contribution of greenhouse gas increases remains challenging. Here, we isolate anthropogenic greenhouse gas impacts on the observed intensification of extreme precipitation during 1951–2015 by comparing observations with CMIP6 individual forcing experiments. Results show that greenhouse gas influences are detected over the global land, Northern Hemisphere extratropics, western and eastern Eurasia, and global “dry” and “wet” regions, which are separable from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most of the observed changes in extreme precipitation intensity, which are consistent with the increased moisture availability with warming. Our results provide the first quantitative evidence for the dominant influence of human‐made greenhouse gases on extreme precipitation increase.
Key Points
Causes of the observed intensification of extreme precipitation during 1951 to 2015 are examined using CMIP6 multimodel simulations
Results from optimal fingerprinting analyses show that the anthropogenic signals are robustly detected at hemispheric and continental scales
The observed increase in extreme precipitation is mostly attributed to anthropogenic greenhouse gas increases
•29-year satellite images were used to explore the evolution of Yellow River Delta.•The morphology of YRD is characterized by a new index defined as delta radius ratio.•The area and coastline of YRD ...increased by 248km2 and 36.45km, respectively.•YRD required sediment loads of ∼441×106tyr−1 before 1996 to maintain equilibrium.•YRD required sediment loads of ∼159×106tyr−1 after 1996 to maintain equilibrium.
Long-term data from a hydrological monitoring station and remotely-sensed satellite images were used to explore the effects of runoff and suspended sediment load on evolution of the Yellow River Delta (YRD) from 1983 to 2011. During this period, an average runoff of 18×109m3yr−1 and an average sediment load of 341×106tyr−1 flowed through the delta lobes into the sea. The runoff and sediment load exhibited downward trends with time, along with large inter-annual fluctuations. Three stages were evident in the data. From 1983 to the late 1990s, the Yellow River experienced progressively severe droughts which reduced both runoff and sediment load to its delta lobe. The delta nevertheless grew to a peak area of about 3950km2 in 2000. From 2000 to 2003, the YRD area decreased. Meanwhile, the operation of the dam at Xiaolangdi and changes in water consumption driven by a new regulatory framework helped stabilize the runoff. Although the sediment load continued to decline, partly due to sediment check dams along the middle Yellow River and the reduced sediment carrying capacity of the river, the YRD area nevertheless increased between 2003 and 2011. The variations in runoff and sediment load directly influenced changes to the plan-form area, shoreline migration, and morphology of the YRD. From 1983 to 2011, the net land area of the delta increased by 248km2, its coastline extended by 36.45km, and its shape became increasingly irregular due to the emergence of its delta lobes. In 1996, an artificial diversion altered the position of the main delta lobe from Qingshuigou to Qing 8. A stepwise multiple regression analysis indicated that the YRD would have required average sediment loads of about 441×106tyr−1 before 1996 and 159×106tyr−1 after 1996 to maintain equilibrium.
Assessing the performance of climate models in surface air temperature (SAT) simulation and projection have received increasing attention during the recent decades. This paper assesses the ...performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating intra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. We evaluate the skill of different multi-model ensemble techniques and use the best technique to project the future SAT changes under different emission scenarios. The results show that most of the general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasia and the difference between the observation and the simulations primarily comes from the winter season. Most of the GCMs can approximately capture the decadal SAT trend; however, the accuracy of annual SAT simulation is relatively low. The correlation coefficient R between each GCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagram shows that the ensemble results generated by the simple model averaging (SMA), reliability ensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to any single GCM output; and the decadal SAT change generated by SMA, REA and BMA are almost identical during 1901-2005. Heuristically, the uncertainty of BMA simulation is the smallest among the three multi-model ensemble simulations. The future SAT projection generated by the BMA shows that the SAT in Northern Eurasia will increase in the 21st century by around 1.03 °C 100 yr, 3.11 °C 100 yr and 7.14 °C 100 yr under the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, the spring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP 4.5 scenarios, while the winter season contributes most to the decadal warming occurring under the RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time in the 21st century.
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