scholarly journals Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales

2021 ◽  
pp. 1-51
Author(s):  
Qiaohong Sun ◽  
Francis Zwiers ◽  
Xuebin Zhang ◽  
Jun Yan
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Spatial Scales ◽  
Coupled Model ◽  
Anthropogenic Influence ◽  
Land Area ◽  
Temporal Dimension ◽  
Time Pattern ◽  
Global Land

AbstractThis study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction or transformation to unitless indices and uses climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy.

Quantifying the human influence on the intensity of extreme 1- and 5-day precipitation amounts at global, continental, and regional scales

2021 ◽  
Author(s):  
Qiaohong Sun ◽  
Francis Zwiers ◽  
Xuebin Zhang ◽  
Jun Yan
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Climate Models ◽  
Spatial Scales ◽  
External Forcing ◽  
Annual Maximum ◽  
Time Pattern ◽  
Detection And Attribution ◽  
Attribution Analysis ◽  
Station Data

<p>Previous detection and attribution analyses suggest that human-induced increases in greenhouse gases have contributed to observed changes in extreme precipitation. However, all previous detection and attribution studies of observed changes in extreme precipitation i) use station data that have been heavily processed via gridding, transformation, and spatial and temporal averaging or other dimension reduction approaches, as well as using climate models to estimate the responses to external forcing, ii) also use models to estimate the unforced natural variability of extreme precipitation. Both aspects reduce user confidence in detection and attribution results.</p><p>We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing and use climate models only to obtain estimates of the space-time pattern of extreme precipitation response to external forcing. We use records of the annual maximum one day (Rx1day) or five consecutive days (Rx5day) precipitation accumulations from 5,081 land-based stations spanning the period 1950-2014 with at least 45 years of coverage, including at least 3 years in the period 2010-2014. Expected responses to external forcings are estimated from ALL and NAT forcings simulations from large ensemble simulations performed with CanESM2 and from a multi-model ensemble that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Changes at these stations are evaluated by fitting non-stationary Generalized Extreme Value (GEV) distributions at individual stations to the logarithms of observed Rx1day and Rx5day values. Non-stationarity in the GEV distribution is permitted by using location parameters that are allowed to be linearly dependent on climate model-simulated responses in ln(Rx1day) and ln(Rx5day) to different forcings. We perform the detection and attribution analysis across different spatial scales, including the global, continental, and regional scales.</p><p>The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (western Northern Hemisphere, western Eurasia, and eastern Eurasia), and many smaller IPCC regions, including C. North-America, E. Asia, E.C. Asia, E. Europe, E. North-America, N. Europe, and W. Siberia for Rx1day, and C. North-America, E. Europe, E. North-America, N. Europe, Russian-Arctic, and W. Siberia for Rx5day. Consistency between our study and previous studies substantially increases confidence in detection and attribution findings concerning extreme precipitation. The attributed effects of anthropogenic forcing on extreme precipitation include substantially decreased waiting times between extreme annual maximum events in regions where anthropogenic influence has been detected and intensification of extreme precipitation that, at a global scale is consistent with the Clausius-Clapeyron rate of about 7% per 1°C of warming.  </p>

scholarly journals Tendency Bias Correction in Coupled and Uncoupled Global Climate Models with a Focus on Impacts over North America

2019 ◽  
Vol 32 (2) ◽  
pp. 639-661 ◽  
Author(s):  
Y. Chang ◽  
S. D. Schubert ◽  
R. D. Koster ◽  
A. M. Molod ◽  
H. Wang
Keyword(s):  
North America ◽  
Bias Correction ◽  
Climate Models ◽  
Global Climate ◽  
Storm Track ◽  
Coupled Model ◽  
Global Climate Models ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Transient Wave

Abstract We revisit the bias correction problem in current climate models, taking advantage of state-of-the-art atmospheric reanalysis data and new data assimilation tools that simplify the estimation of short-term (6 hourly) atmospheric tendency errors. The focus is on the extent to which correcting biases in atmospheric tendencies improves the model’s climatology, variability, and ultimately forecast skill at subseasonal and seasonal time scales. Results are presented for the NASA GMAO GEOS model in both uncoupled (atmosphere only) and coupled (atmosphere–ocean) modes. For the uncoupled model, the focus is on correcting a stunted North Pacific jet and a dry bias over the central United States during boreal summer—long-standing errors that are indeed common to many current AGCMs. The results show that the tendency bias correction (TBC) eliminates the jet bias and substantially increases the precipitation over the Great Plains. These changes are accompanied by much improved (increased) storm-track activity throughout the northern midlatitudes. For the coupled model, the atmospheric TBCs produce substantial improvements in the simulated mean climate and its variability, including a much reduced SST warm bias, more realistic ENSO-related SST variability and teleconnections, and much improved subtropical jets and related submonthly transient wave activity. Despite these improvements, the improvement in subseasonal and seasonal forecast skill over North America is only modest at best. The reasons for this, which are presumably relevant to any forecast system, involve the competing influences of predictability loss with time and the time it takes for climate drift to first have a significant impact on forecast skill.

Human Influences on Changes in the Temperature Seasonality in Mid- to High-Latitude Land Areas

2015 ◽  
Vol 28 (15) ◽  
pp. 5908-5921 ◽  
Author(s):  
Cheng Qian ◽  
Xuebin Zhang
Keyword(s):  
Annual Cycle ◽  
High Latitude ◽  
Climate Models ◽  
Regional Scale ◽  
Coupled Model ◽  
Surface Air Temperature ◽  
High Latitudes ◽  
Time Pattern ◽  
Detection And Attribution ◽  
Temperature Seasonality

Abstract The annual cycle is the largest variability for many climate variables outside the tropics. Whether human activities have affected the annual cycle at the regional scale is unclear. In this study, long-term changes in the amplitude of surface air temperature annual cycle in the observations are compared with those simulated by the climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Different spatial domains ranging from hemispheric to subcontinental scales in mid- to high-latitude land areas for the period 1950–2005 are considered. Both the optimal fingerprinting and a nonoptimal detection and attribution technique are used. The results show that the space–time pattern of model-simulated responses to the combined effect of anthropogenic and natural forcings is consistent with the observed changes. In particular, models capture not only the decrease in the temperature seasonality in the northern high latitudes and East Asia, but also the increase in the Mediterranean region. A human influence on the weakening in the temperature seasonality in the Northern Hemisphere is detected, particularly in the high latitudes (50°–70°N) where the influence of the anthropogenic forcing can be separated from that of the natural forcing.

Did tropical precipitation improve in CMIP6 simulations?

2020 ◽  
Author(s):  
Stephanie Fiedler ◽  
Traute Crueger ◽  
Roberta D’Agostino ◽  
Karsten Peters ◽  
Tobias Becker ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Southern Oscillation ◽  
Spatial Scales ◽  
Precipitation Amount ◽  
Coupled Model ◽  
Internal Variability ◽  
Atmospheric Composition ◽  
Tropical Precipitation ◽  
General Aspects

<p>Climate models are known to have biases in tropical precipitation. We assessed to what extent simulations of tropical precipitation have improved in the new Coupled Model Intercomparison Project (CMIP) phase six, using state-of-the-art observational products and model results from the earlier CMIP phases three and five. We characterize tropical precipitation with different well-established metrics. Our assessment includes (1) general aspects of the mean climatology like precipitation associated with the Intertropical Convergence Zone and shallow cloud regimes in the tropics, (2) solar radiative effects including the summer monsoons and the time of occurrence of tropical precipitation in the course of the day, (3) modes of internal variability such as the Madden-Julian Oscillation and the El Niño Southern Oscillation, and (4) changes in the course of the 20th century. The results point to improvements of CMIP6 models for some metrics, e.g., the occurrence of drizzle events and consecutive dry days. However, no improvements of CMIP6 models are identified for other aspects of tropical precipitation. These include the area and intensity of the global summer monsoon as well as the diurnal cycle of the tropical precipitation amount, frequency and intensity.</p><p>All our metrics taken together, CMIP6 models show no systematic improvement of tropical precipitation across different temporal and spatial scales. The model biases in the spatial distribution of tropical precipitation are typically larger than the changes associated with anthropogenic warming. Given the pace of climate change as compared to the pace of climate model improvements, we suggest to use novel modeling approaches to understand the responseof tropical precipitation to changes in atmospheric composition.</p>

Merged Statistical Analyses of Historical Monthly Precipitation Anomalies Beginning 1900

2010 ◽  
Vol 23 (21) ◽  
pp. 5755-5770 ◽  
Author(s):  
Thomas M. Smith ◽  
Phillip A. Arkin ◽  
Mathew R. P. Sapiano ◽  
Ching-Yee Chang
Keyword(s):  
Twentieth Century ◽  
Time Scales ◽  
Large Scale ◽  
Climate Models ◽  
Spatial Scales ◽  
Phase Relationship ◽  
Coupled Model ◽  
Data Availability ◽  
Monthly Precipitation ◽  
Interannual Variations

Abstract A monthly reconstruction of precipitation beginning in 1900 is presented. The reconstruction resolves interannual and longer time scales and spatial scales larger than 5° over both land and oceans. Because of different land and ocean data availability, the reconstruction combines two separate historical reconstructions. One analyzes interannual variations directly by fitting gauge-based anomalies to large-scale spatial modes. This direct reconstruction is used for land anomalies and interannual oceanic anomalies. The other analyzes annual and longer variations indirectly from correlations with analyzed sea surface temperature and sea level pressure. This indirect reconstruction is used for oceanic variations with time scales longer than interannual. In addition, a method of estimating reconstruction errors is also presented. Over land the reconstruction is a filtered representation of the gauge data with data gaps filled. Over oceans the reconstruction gives an estimate of the atmospheric response to changing temperature and pressure, combined with interannual variations. The reconstruction makes it possible to evaluate global precipitation variations for periods much longer than the satellite period, which begins in 1979. Evaluations show some large-scale similarities with coupled model precipitation variations over the twentieth century, including an increasing tendency over the century. The reconstructed land and sea trends tend to be out of phase at low latitudes, similar to the out-of-phase relationship for interannual variations. This reconstruction may be used for climate monitoring, for statistical climate studies of the twentieth century, and for helping to evaluate dynamic climate models. In the future the possibility of improving the reconstruction will be explored by further improving the analysis methods and including additional data.

scholarly journals Projected Future Changes in Vegetation in Western North America in the Twenty-First Century

2013 ◽  
Vol 26 (11) ◽  
pp. 3671-3687 ◽  
Author(s):  
Xiaoyan Jiang ◽  
Sara A. Rauscher ◽  
Todd D. Ringler ◽  
David M. Lawrence ◽  
A. Park Williams ◽  
...  
Keyword(s):  
North America ◽  
Climate Models ◽  
Coupled Model ◽  
First Century ◽  
Earth System ◽  
Western North America ◽  
Dynamic Global Vegetation Model ◽  
Future Warming ◽  
Twenty First Century ◽  
Tree Coverage

Abstract Rapid and broad-scale forest mortality associated with recent droughts, rising temperature, and insect outbreaks has been observed over western North America (NA). Climate models project additional future warming and increasing drought and water stress for this region. To assess future potential changes in vegetation distributions in western NA, the Community Earth System Model (CESM) coupled with its Dynamic Global Vegetation Model (DGVM) was used under the future A2 emissions scenario. To better span uncertainties in future climate, eight sea surface temperature (SST) projections provided by phase 3 of the Coupled Model Intercomparison Project (CMIP3) were employed as boundary conditions. There is a broad consensus among the simulations, despite differences in the simulated climate trajectories across the ensemble, that about half of the needleleaf evergreen tree coverage (from 24% to 11%) will disappear, coincident with a 14% (from 11% to 25%) increase in shrubs and grasses by the end of the twenty-first century in western NA, with most of the change occurring over the latter half of the twenty-first century. The net impact is a ~6 GtC or about 50% decrease in projected ecosystem carbon storage in this region. The findings suggest a potential for a widespread shift from tree-dominated landscapes to shrub and grass-dominated landscapes in western NA because of future warming and consequent increases in water deficits. These results highlight the need for improved process-based understanding of vegetation dynamics, particularly including mortality and the subsequent incorporation of these mechanisms into earth system models to better quantify the vulnerability of western NA forests under climate change.

Understanding future increases in precipitation extremes in global land monsoon regions

2021 ◽  
pp. 1-38
Keyword(s):  
Climate Models ◽  
Daily Precipitation ◽  
Coupled Model ◽  
Social Benefit ◽  
Precipitation Extremes ◽  
Historical Period ◽  
Dynamic Scaling ◽  
Continuous Increase ◽  
Global Land ◽  
Thermodynamics And Dynamics

Abstract This study investigates future changes in daily precipitation extremes and the involved physics over the global land monsoon (GM) region using climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The daily precipitation extreme is identified by the cutoff scale, measuring the extreme tail of the precipitation distribution. Compared to the historical period, multi-model results reveal a continuous increase in precipitation extremes under four scenarios, with a progressively higher fraction of precipitation exceeding the historical cutoff scale when moving into the future. The rise of the cutoff-scale by the end of the century is reduced by 57.8% in the moderate emission scenario relative to the highest scenario, underscoring the social benefit in reducing emissions. The cutoff scale sensitivity, defined by the increasing rates of the cutoff scale over the GM region to the global mean surface temperature increase, is nearly independent of the projected periods and emission scenarios, roughly 8.0% K−1 by averaging all periods and scenarios. To understand the cause of the changes, we applied a physical scaling diagnostic to decompose them into thermodynamic and dynamic contributions. We find that thermodynamics and dynamics have comparable contributions to the intensified precipitation extremes in the GM region. Changes in thermodynamic scaling contribute to a spatially uniform increase pattern, while changes in dynamic scaling dominate the regional differences in the increased precipitation extremes. Furthermore, the large inter-model spread of the projection is primarily attributed to variations of dynamic scaling among models.

scholarly journals Simple scaling of extreme precipitation in North America

2017 ◽  
Vol 21 (11) ◽  
pp. 5823-5846 ◽  
Author(s):  
Silvia Innocenti ◽  
Alain Mailhot ◽  
Anne Frigon
Keyword(s):  
North America ◽  
Extreme Precipitation ◽  
Probability Distributions ◽  
Spatial Scales ◽  
Extreme Rainfall ◽  
Parameter Estimates ◽  
Infrastructure Design ◽  
Simple Scaling ◽  
Intensity Duration Frequency ◽  
Extreme Probability

Abstract. Extreme precipitation is highly variable in space and time. It is therefore important to characterize precipitation intensity distributions on several temporal and spatial scales. This is a key issue in infrastructure design and risk analysis, for which intensity–duration–frequency (IDF) curves are the standard tools used for describing the relationships among extreme rainfall intensities, their frequencies, and their durations. Simple scaling (SS) models, characterizing the relationships among extreme probability distributions at several durations, represent a powerful means for improving IDF estimates. This study tested SS models for approximately 2700 stations in North America. Annual maximum series (AMS) over various duration intervals from 15 min to 7 days were considered. The range of validity, magnitude, and spatial variability of the estimated scaling exponents were investigated. Results provide additional guidance for the influence of both local geographical characteristics, such as topography, and regional climatic features on precipitation scaling. Generalized extreme-value (GEV) distributions based on SS models were also examined. Results demonstrate an improvement of GEV parameter estimates, especially for the shape parameter, when data from different durations were pooled under the SS hypothesis.

scholarly journals Detection of Human Influence on Precipitation Extremes in Asia

2020 ◽  
Vol 33 (12) ◽  
pp. 5293-5304 ◽  
Author(s):  
Siyan Dong ◽  
Ying Sun ◽  
Chao Li
Keyword(s):  
High Latitude ◽  
Extreme Precipitation ◽  
Signal Analysis ◽  
Coupled Model ◽  
Internal Variability ◽  
Precipitation Extremes ◽  
Anthropogenic Influence ◽  
Significance Level ◽  
External Forcings ◽  
Extreme Indices

AbstractThis paper examines the possible influence of external forcings on observed changes in precipitation extremes in the mid-to-high latitudes of Asia during 1958–2012 and attempts to identify particular extreme precipitation indices on which there are better chances to detect the influence of external forcings. We compare a recently compiled dataset of observed extreme indices with those from phase 5 of the Coupled Model Intercomparison Project (CMIP5) simulations using an optimal fingerprinting method. We consider six indices that characterize different aspects of extreme precipitation, including annual maximum amount of precipitation falling in 1 day (Rx1day) or 5 days (Rx5day), the total amount of precipitation from the top 5% or top 1% daily amount on wet days, and the fraction of the annual total precipitation from these events. For single-signal analysis, the fingerprints of external forcings including anthropogenic agents are robustly detected in most studied extreme indices over all Asia and for midlatitude Asia but not for high-latitude Asia. For two-signal analysis, anthropogenic influence is detectable in these indices over Asia at 5% or slightly less than 5% significance level, whereas natural influence is not detectable. In high-latitude Asia, anthropogenic influence is detected only in a fractional index, representing a stark contrast to the midlatitude and full Asia results. We find relatively smaller internal variability and thus higher signal-to-noise ratio in the fractional indices when compared with the other ones. Our results point to the need for studying precipitation extreme indices that are less affected by internal variability while still representing the relevant nature of precipitation extremes to improve the possibility of detecting a forced signal if one is present in the data.

scholarly journals Extreme Events of Precipitation over Complex Terrain Derived from Satellite Data for Climate Applications: An Evaluation of the Southern Slopes of the Pyrenees

2020 ◽  
Vol 12 (13) ◽  
pp. 2171
Author(s):  
Andrés Navarro ◽  
Eduardo García-Ortega ◽  
Andrés Merino ◽  
José Luis Sánchez
Keyword(s):  
Satellite Data ◽  
Extreme Precipitation ◽  
Complex Terrain ◽  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Ground Truth ◽  
Extreme Precipitation Events ◽  
Precipitation Measurement ◽  
Global Precipitation

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation.

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