scholarly journals Assessing the Robustness of Future Extreme Precipitation Intensification in the CMIP5 Ensemble

2018 ◽  
Vol 31 (16) ◽  
pp. 6505-6525 ◽  
Author(s):  
Margot Bador ◽  
Markus G. Donat ◽  
Olivier Geoffroy ◽  
Lisa V. Alexander

Abstract A warming climate is expected to intensify extreme precipitation, and climate models project a general intensification of annual extreme precipitation in most regions of the globe throughout the twenty-first century. We investigate the robustness of this future intensification over land across different models, regions, and seasons and evaluate the role of model interdependencies in the CMIP5 ensemble. Strong similarities in extreme precipitation changes are found between models that share atmospheric physics, turning an ensemble of 27 models into around 14 projections. We find that future annual extreme precipitation intensity increases in the majority of models and in the majority of land grid cells, from the driest to the wettest regions, as defined by each model’s precipitation climatology. The intermodel spread is generally larger over wet than over dry regions, smaller in the dry season compared to the wet season and at the annual scale, and largely reduced in extratropical compared to tropical regions and at the global scale. For each model, the future increase in annual and seasonal maximum daily precipitation amounts exceeds the range of simulated internal variability in the majority of land grid cells. At both annual and seasonal scales, however, there are a few regions where the change is still within the background climate noise, but their size and location differ between models. In extratropical regions, the signal-to-noise ratio of projected changes in extreme precipitation is particularly robust across models because of a similar change and background climate noise, whereas projected changes are less robust in the tropics.

2017 ◽  
Vol 30 (24) ◽  
pp. 9949-9964 ◽  
Author(s):  
Aleksandra Borodina ◽  
Erich M Fischer ◽  
Reto Knutti

Projected changes in temperature extremes, such as regional changes in the intensity and frequency of hot extremes, differ strongly across climate models. This study shows that this disagreement can be partly explained by discrepancies in the representation of the present-day temperature distribution, motivating the evaluation of models with observations. By evaluating climate models on carefully selected metrics, the models that are more likely to be reliable for long-term projections of temperature extremes are identified. The study found that frequencies of hot extremes are likely to increase at a higher rate than the multimodel mean estimate over large parts of the Northern Hemisphere and Australia. This implies that a higher degree of adaptation is required for a given global temperature target. It also found that projected changes in the intensity of hot extremes can be constrained in several regions, including Australia, central North America, and north Asia. In many other regions, large internal variability can often hamper model evaluation. For both aspects—the intensity and the frequency of hot extremes—the total area over which the constraints can be implemented is limited by the quality and completeness of observations. Thereby, this study highlights the importance of long-term, high-quality, and easily accessible observational records for model evaluation, which are vital to ultimately reduce uncertainties in projections of temperature extremes.


2019 ◽  
Vol 5 (4) ◽  
pp. 308-321 ◽  
Author(s):  
Xiao-Tong Zheng

Abstract Purpose of Review Understanding the changes in climate variability in a warming climate is crucial for reliable projections of future climate change. This article reviews the recent progress in studies of how climate modes in the Indo-Pacific respond to greenhouse warming, including the consensus and uncertainty across climate models. Recent Findings Recent studies revealed a range of robust changes in the properties of climate modes, often associated with the mean state changes in the tropical Indo-Pacific. In particular, the intermodel diversity in the ocean warming pattern is a prominent source of uncertainty in mode changes. The internal variability also plays an important role in projected changes in climate modes. Summary Model biases and intermodel variability remain major challenges for reducing uncertainty in projecting climate mode changes in warming climate. Improved models and research linking simulated present-day climate and future changes are essential for reliable projections of climate mode changes. In addition, large ensembles should be used for each model to reduce the uncertainty from internal variability and isolate the forced response to global warming.


Author(s):  
A. N. Gelfan ◽  
V. A. Semenov ◽  
Yu. G. Motovilov

Abstract. An approach has been proposed to analyze the simulated hydrological extreme uncertainty related to the internal variability of the atmosphere ("climate noise"), which is inherent to the climate system and considered as the lowest level of uncertainty achievable in climate impact studies. To assess the climate noise effect, numerical experiments were made with climate model ECHAM5 and hydrological model ECOMAG. The case study was carried out to Northern Dvina River basin (catchment area is 360 000 km2), whose hydrological regime is characterised by extreme freshets during spring-summer snowmelt period. The climate noise was represented by ensemble ECHAM5 simulations (45 ensemble members) with identical historical boundary forcing and varying initial conditions. An ensemble of the ECHAM5-outputs for the period of 1979–2012 was used (after bias correction post-processing) as the hydrological model inputs, and the corresponding ensemble of 45 multi-year hydrographs was simulated. From this ensemble, we derived flood statistic uncertainty caused by the internal variability of the atmosphere.


2015 ◽  
Vol 19 (2) ◽  
pp. 877-891 ◽  
Author(s):  
B. Asadieh ◽  
N. Y. Krakauer

Abstract. Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally in comparable ways. We find that both observations and models show generally increasing trends in extreme precipitation since 1901, with the largest changes in the deep tropics. Annual-maximum daily precipitation (Rx1day) has increased faster in the observations than in most of the CMIP5 models. On a global scale, the observational annual-maximum daily precipitation has increased by an average of 5.73 mm over the last 110 years, or 8.5% in relative terms. This corresponds to an increase of 10% K−1 in global warming since 1901, which is larger than the average of climate models, with 8.3% K−1. The average rate of increase in extreme precipitation per K of warming in both models and observations is higher than the rate of increase in atmospheric water vapor content per K of warming expected from the Clausius–Clapeyron equation. We expect our findings to help inform assessments of precipitation-related hazards such as flooding, droughts and storms.


2021 ◽  
Author(s):  
Gavin D. Madakumbura ◽  
Chad W. Thackeray ◽  
Jesse Norris ◽  
Naomi Goldenson ◽  
Alex Hall

Abstract Global climate models produce large increases in extreme precipitation when subject to anthropogenic forcing, but detecting this human influence in observations is challenging. Large internal variability makes the signal difficult to characterize. Models produce diverse precipitation responses to anthropogenic forcing, mirroring a variety of parameterization choices for subgrid-scale processes. And observations are inhomogeneously sampled in space and time, leading to multiple global datasets, each produced with a different homogenization technique. Thus, previous attempts to detect human influence on extreme precipitation have not incorporated internal variability or model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods, we find a physically interpretable anthropogenic signal that is detectable in all global datasets. Detection occurs even when internal variability and model uncertainty are taken into account. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in extreme precipitation.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Gavin D. Madakumbura ◽  
Chad W. Thackeray ◽  
Jesse Norris ◽  
Naomi Goldenson ◽  
Alex Hall

Abstract The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.


2021 ◽  
Author(s):  
Gavin D. Madakumbura ◽  
Chad W. Thackeray ◽  
Jesse Norris ◽  
Naomi Goldenson ◽  
Alex Hall

Abstract The intensification of extreme precipitation under anthropogenic forcing is robustly projected by global climate models, but highly challenging to detect in the observational record. Large internal variability distorts this anthropogenic signal. Models produce diverse magnitudes of precipitation response to anthropogenic forcing, largely due to differing schemes for parameterizing subgrid-scale processes. Meanwhile, multiple global observational datasets of daily precipitation exist, developed using varying techniques and inhomogeneously sampled data in space and time. Previous attempts to detect human influence on extreme precipitation have not incorporated model uncertainty, and have been limited to specific regions and observational datasets. Using machine learning methods that can account for these uncertainties and capable of identifying the time evolution of the spatial patterns, we find a physically interpretable anthropogenic signal that is detectable in all global observational datasets. Machine learning efficiently generates multiple lines of evidence supporting detection of an anthropogenic signal in global extreme precipitation.


2020 ◽  
Author(s):  
Julia Maschler ◽  
Daniel S. Maynard ◽  
Devin Routh ◽  
Johan van den Hoogen ◽  
Zhaolei Li ◽  
...  

<p>Soil nitrogen is a prominent determinant of plant growth, with nitrogen (N) availability being a key driver of terrestrial carbon sequestration. The local availability of soil N is thus crucial to our understanding of broad-scale trends in soil fertility, productivity, and carbon dynamics. Here, we provide global, high-resolution maps of current and future (2050) potential net nitrogen mineralization (N-min), revealing global patterns in soil N availability. Highest mineralization rates are found in warm and moist tropical regions, leading to a strong latitudinal gradient in N-min. We observed a positive correlation of N-min rates with human population density and net primary productivity. Projected climate conditions for 2050 suggest that N availability will further decrease in areas of low N availability and increase in areas of high N availability, thereby intensifying current global trends. These results shed light on the core processes governing productivity at a global scale, providing an opportunity to improve the accuracy of plant biomass and climate models.</p>


2015 ◽  
Vol 28 (16) ◽  
pp. 6324-6334 ◽  
Author(s):  
Neil Berg ◽  
Alex Hall

Abstract Changes to mean and extreme wet season precipitation over California on interannual time scales are analyzed using twenty-first-century precipitation data from 34 global climate models. Models disagree on the sign of projected changes in mean precipitation, although in most models the change is very small compared to historical and simulated levels of interannual variability. For the 2020/21–2059/60 period, there is no projected increase in the frequency of extremely dry wet seasons in the ensemble mean. Wet extremes are found to increase to around 2 times the historical frequency, which is statistically significant at the 95% level. Stronger signals emerge in the 2060/61–2099/2100 period. Across all models, extremely dry wet seasons are roughly 1.5 to 2 times more common, and wet extremes generally triple in their historical frequency (statistically significant). Large increases in precipitation variability in most models account for the modest increases to dry extremes. Increases in the frequency of wet extremes can be ascribed to equal contributions from increased variability and increases to the mean. These increases in the frequency of interannual precipitation extremes will create severe water management problems in a region where coping with large interannual variability in precipitation is already a challenge. Evidence from models and observations is examined to understand the causes of the low precipitation associated with the 2013/14 drought in California. These lines of evidence all strongly indicate that the low 2013/14 wet season precipitation total can be very likely attributed to natural variability, in spite of the projected future changes in extremes.


2021 ◽  
Author(s):  
Dave Rowell ◽  
Segolene Berthou

<p>Regional climate projections using ultra-high resolution convection-permitting (CP) models are now increasingly available, with recent endeavours also focussing on vulnerable tropical regions. A number of recent studies have examined a pair of pan-Africa integrations of the Met Office CP model (CP4A), run at 4.4km resolution with 10 years of both a present-day simulation and a circa-2100 projection. However, experience from inter-disciplinary discussions has revealed different perspectives on the value of such experiments, with climate scientists emphasising the importance of an improved representation of convection, whereas applied scientists emphasise the importance of the unprecedented spatial scale of the available climate data. This raises critical questions about the usable spatial scales of such projections. Can CP models really provide robust information about future climate change at finer scales than parameterised regional climate models? We address this question with a focus on projected changes in rainfall, both seasonal means and daily extremes, both of which may be expected to exhibit heterogeneous climate responses in regions of large surface forcing. Although the capacity for statistically significant detail is found to be small in this short projection, detectable sub-25km variability is indeed apparent in regions of high topographic variability. Coastal regions, such as lakes and marine bays are also examined, along with urban boundaries. Furthermore, where no significant fine-scale detail is apparent (spatial heterogeneity is only due to sampling variability), we also examine the extent to which the robustness of climate information (better signal-to-noise ratios) can be enhanced for users by the spatial aggregation of model data.</p>


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