scholarly journals Dry and moist dynamics shape regional patterns of extreme precipitation sensitivity

2020 ◽  
Vol 117 (16) ◽  
pp. 8757-8763 ◽  
Author(s):  
Ji Nie ◽  
Panxi Dai ◽  
Adam H. Sobel
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Climate Model ◽  
Regional Scale ◽  
Vertical Motion ◽  
Precipitable Water ◽  
Small Scale ◽  
Climate Projections ◽  
Regional Patterns

Responses of extreme precipitation to global warming are of great importance to society and ecosystems. Although observations and climate projections indicate a general intensification of extreme precipitation with warming on global scale, there are significant variations on the regional scale, mainly due to changes in the vertical motion associated with extreme precipitation. Here, we apply quasigeostrophic diagnostics on climate-model simulations to understand the changes in vertical motion, quantifying the roles of dry (large-scale adiabatic flow) and moist (small-scale convection) dynamics in shaping the regional patterns of extreme precipitation sensitivity (EPS). The dry component weakens in the subtropics but strengthens in the middle and high latitudes; the moist component accounts for the positive centers of EPS in the low latitudes and also contributes to the negative centers in the subtropics. A theoretical model depicts a nonlinear relationship between the diabatic heating feedback (α) and precipitable water, indicating high sensitivity of α (thus, EPS) over climatological moist regions. The model also captures the change of α due to competing effects of increases in precipitable water and dry static stability under global warming. Thus, the dry/moist decomposition provides a quantitive and intuitive explanation of the main regional features of EPS.

Impact of regional climate model input and soil map resolution on projected winter wheat production in SW Germany

2020 ◽  
Author(s):  
Sebastian Gayler ◽  
Rajina Bajracharya ◽  
Tobias Weber ◽  
Thilo Streck
Keyword(s):  
High Resolution ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Scale ◽  
Grid Cell ◽  
Small Scale ◽  
Climate Projections ◽  
Soil Map ◽  
Soil Information ◽  
The Impact

<p>Agricultural ecosystem models, driven by climate projections and fed with soil information and plausible management scenarios are frequently used tools to predict future developments in agricultural landscapes. On the regional scale, the required soil parameters must be derived from soil maps that are available in different spatial resolutions, ranging from grid cell sizes of 50 m up to 1 km and more. The typical spatial resolution of regional climate projections is currently around 12 km. Given the small-scale heterogeneity in soil properties, using the most accurate soil representation could be important for predictions of crop growth. However, simulations with very highly resolved soil data requires greater computing time and higher effort for data organization and storage. Moreover, the higher resolution may not necessarily lead to better simulations due to redundant information of the land surface and because the impact of climate forcing could dominate over the effect of soil variability. This leads to the question if the use of high-resolution soil data leads to significantly different predictions of future yields and grain protein trends compared to simulations in which soil data is adapted to the resolution of the climate input.</p><p>This study investigated the impact of weather and soil input on simulated crop growth in an intensively used agricultural region in Southwest Germany. For all areas classified as ‘arable land’ (CLC10), winter wheat growth was simulated over a 44-year period (2006 to 2050) using weather projections from three regional climate models and soil information at two spatial resolutions. The simulations were performed with the model system Expert-N 5.0, where the crop model Gecros was combined with the Richards equation and the CN turnover module of the model Daisy. Soil hydraulic parameters as well as initial values of soil organic matter pools were estimated from BK50 soil map information on soil texture and soil organic matter content, using pedo-transfer functions and SOM pool fractionation following Bruun and Jensen (2002). The coarser soil map is derived from BK50 soil map (50m x 50m) by selecting only the dominant soil type in a 12km × 12km grid to be representative for that grid cell. The crop model was calibrated with field data of crop phenology, leaf area, biomass, yield and crop nitrogen, which were collected at a research station within the study area between 2009 and 2018.</p><p>The predicted increase in temperatures during the growing season correlated with earlier maturity, lower yields and a higher grain protein content. The regional mean values varied by +/- 0.5 t/ha or +/-0.3 percentage points of protein content depending to the climate model used. On the regional scale, the simulated trends remained unchanged using high-resolution or coarse resolution soil data. However, there are strong differences in both the forecasted averages and the distribution of forecasts, as the coarser resolution captures neither the small-scale heterogeneity nor the average of the high-resolution results.</p>

scholarly journals Emergent Behavior and Uncertainty in Multimodel Climate Projections of Precipitation Trends at Small Spatial Scales

2006 ◽  
Vol 19 (21) ◽  
pp. 5554-5569 ◽  
Author(s):  
P. Good ◽  
J. Lowe
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Scale Effects ◽  
Spatial Scales ◽  
Regional Scale ◽  
Emergent Behavior ◽  
Small Scale ◽  
Climate Projections ◽  
New Methods ◽  
Precipitation Trends

Abstract Aspects of model emergent behavior and uncertainty in regional- and small-scale effects of increasing CO2 on seasonal (June–August) precipitation are explored. Nineteen different climate models are studied. New methods of comparing multiple climate models reveal a clearer and more impact-relevant view of precipitation projections for the current century. First, the importance of small spatial scales in multimodel projections is demonstrated. Local trends can be much larger than or even have an opposing sign to the large-scale regional averages used in previous studies. Small-scale effects of increasing CO2 and natural internal variability both play important roles here. These small-scale features make multimodel comparisons difficult for precipitation. New methods that allow information from small spatial scales to be usefully compared across an ensemble of multiple models are presented. The analysis philosophy of this study works with statistical distributions of small-scale variations within climatological regions. A major result of this work is a set of emergent relationships coupling the small- and regional-scale effects of CO2 on precipitation trends. Within each region, a single relationship fits the ensemble of 19 different climate models. Using these relationships, a surprisingly large part of the intermodel variance in small-scale effects of CO2 is explainable simply by the intermodel variance in the regional mean (a form of pattern scaling). Different regions show distinctly different relationships. These relationships imply that regional mean results are still useful, as long as the interregional variation in their relationship with impact-relevant extreme trends is recognized. These relationships are used to present a clear but rich picture of an aspect of model uncertainty, characterized by the intermodel spread in seasonal precipitation trends, including information from small spatial scales.

scholarly journals Spatial-Scale Dependence of Climate Model Performance in the CMIP3 Ensemble

2011 ◽  
Vol 24 (11) ◽  
pp. 2680-2692 ◽  
Author(s):  
David Masson ◽  
Reto Knutti
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Model ◽  
Spatial Scales ◽  
Global Climate Models ◽  
Small Scale ◽  
Climate Projections ◽  
Model Errors ◽  
Climate Signal ◽  
Temperature And Precipitation

Abstract About 20 global climate models have been run for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) to predict climate change due to anthropogenic activities. Evaluating these models is an important step to establish confidence in climate projections. Model evaluation, however, is often performed on a gridpoint basis despite the fact that models are known to often be unreliable at such small spatial scales. In this study, the annual mean values of surface air temperature and precipitation are analyzed. Using a spatial smoothing technique with a variable-scale parameter it is shown that the intermodel spread, as well as model errors from observations, is reduced as the characteristic smoothing scale increases. At the same time, the ability to reproduce small-scale features is reduced and the simulated patterns become fuzzy. Depending on the variable of interest, the location, and the way that data are aggregated, different optimal smoothing scales from the gridpoint size to about 2000 km are found to give good agreement with present-day observation yet retain most regional features of the climate signal. Higher model resolution surprisingly does not imply much better agreement with temperature observations, in particular with stronger smoothing, and resolving smaller scales therefore does not necessarily seem to improve the simulation of large-scale climate features. Similarities in mean temperature and precipitation fields for a pair of models in the ensemble persist locally for about a century into the future, providing some justification for subtracting control errors in the models. Large-scale to global errors, however, are not well preserved over time, consistent with a poor constraint of the present-day climate on the simulated global temperature and precipitation response.

scholarly journals Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

2017 ◽  
Vol 10 (3) ◽  
pp. 1383-1402 ◽  
Author(s):  
Paolo Davini ◽  
Jost von Hardenberg ◽  
Susanna Corti ◽  
Hannah M. Christensen ◽  
Stephan Juricke ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Rainfall Variability ◽  
Global Climate Model ◽  
Small Scale ◽  
Low Resolution ◽  
The Impact ◽  
Stochastic Physics

Abstract. The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979–2008) and a climate change projection (2039–2068), together with coupled transient runs (1850–2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate – specifically the Madden–Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

Evaluating the Consistency between Statistically Downscaled and Global Dynamical Model Climate Change Projections

2008 ◽  
Vol 21 (22) ◽  
pp. 6052-6059 ◽  
Author(s):  
B. Timbal ◽  
P. Hope ◽  
S. Charles
Keyword(s):  
Global Warming ◽  
Statistical Downscaling ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Specific Humidity ◽  
Model Output ◽  
Winter Rainfall ◽  
Direct Model ◽  
The Impact

Abstract The consistency between rainfall projections obtained from direct climate model output and statistical downscaling is evaluated. Results are averaged across an area large enough to overcome the difference in spatial scale between these two types of projections and thus make the comparison meaningful. Undertaking the comparison using a suite of state-of-the-art coupled climate models for two forcing scenarios presents a unique opportunity to test whether statistical linkages established between large-scale predictors and local rainfall under current climate remain valid in future climatic conditions. The study focuses on the southwest corner of Western Australia, a region that has experienced recent winter rainfall declines and for which climate models project, with great consistency, further winter rainfall reductions due to global warming. Results show that as a first approximation the magnitude of the modeled rainfall decline in this region is linearly related to the model global warming (a reduction of about 9% per degree), thus linking future rainfall declines to future emission paths. Two statistical downscaling techniques are used to investigate the influence of the choice of technique on projection consistency. In addition, one of the techniques was assessed using different large-scale forcings, to investigate the impact of large-scale predictor selection. Downscaled and direct model projections are consistent across the large number of models and two scenarios considered; that is, there is no tendency for either to be biased; and only a small hint that large rainfall declines are reduced in downscaled projections. Among the two techniques, a nonhomogeneous hidden Markov model provides greater consistency with climate models than an analog approach. Differences were due to the choice of the optimal combination of predictors. Thus statistically downscaled projections require careful choice of large-scale predictors in order to be consistent with physically based rainfall projections. In particular it was noted that a relative humidity moisture predictor, rather than specific humidity, was needed for downscaled projections to be consistent with direct model output projections.

Response of Tropical Cyclone Potential Intensity to a Global Warming Scenario in the IPCC AR4 CGCMs

2010 ◽  
Vol 23 (6) ◽  
pp. 1354-1373 ◽  
Author(s):  
Jinhua Yu ◽  
Yuqing Wang ◽  
Kevin Hamilton
Keyword(s):  
Global Warming ◽  
Tropical Cyclone ◽  
Large Scale ◽  
Linear Trend ◽  
Relative Effect ◽  
Vertical Shear ◽  
Model Parameters ◽  
Climate Projections ◽  
South Indian ◽  
Projected Changes

Abstract This paper reports on an analysis of the tropical cyclone (TC) potential intensity (PI) and its control parameters in transient global warming simulations. Specifically, the TC PI is calculated for phase 3 of the Coupled Model Intercomparison Project (CMIP3) integrations during the first 70 yr of a transient run forced by a 1% yr−1 CO2 increase. The linear trend over the period is used to project a 70-yr change in relevant model parameters. The results for a 15-model ensemble-mean climate projection show that the thermodynamic potential intensity (THPI) increases on average by 1.0% to ∼3.1% over various TC basins, which is mainly attributed to changes in the disequilibrium in enthalpy between the ocean and atmosphere in the transient response to increasing CO2 concentrations. This modest projected increase in THPI is consistent with that found in other recent studies. In this paper the effects of evolving large-scale dynamical factors on the projected TC PI are also quantified, using an empirical formation that takes into account the effects of vertical shear and translational speed based on a statistical analysis of present-day observations. Including the dynamical efficiency in the formulation of PI leads to larger projected changes in PI relative to that obtained using just THPI in some basins and smaller projected changes in others. The inclusion of the dynamical efficiency has the largest relative effect in the main development region (MDR) of the North Atlantic, where it leads to a 50% reduction in the projected PI change. Results are also presented for the basin-averaged changes in PI for the climate projections from each of the 15 individual models. There is considerable variation among the results for individual model projections, and for some models the projected increase in PI in the eastern Pacific and south Indian Ocean regions exceeds 10%.

Changes in Yangtze Precipitation Extremes and the association with thermodynamic, dynamic, and local drivers  

2021 ◽  
Author(s):  
Xin Li
Keyword(s):  
Global Warming ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Precipitation Extremes ◽  
Spatiotemporal Variability ◽  
Local Effects ◽  
Dynamic Factors ◽  
The Yangtze River Basin

<p>Spatioteporal variability of precipitation extremes is increasingly the focus of attention in both the climate and hydrology communites, especailly in the context of global climate change. Indicated by the Clausius-Clapeyron equation under the constant relative humudity assumption, it is expected, from the thermodynamic perspective, that extreme precipitation would increase as globe warms. However, when it comes to the regional response of precipitation to global warming, the resutls could be highly uncertain due to the influences of dynamic factors such as large-scale circlation patterns and local effects. Here, we investigate trends in a set of extreme precipitation indices (EPIs) over the Yangtze River Basin (YRB) during the period of 1960-2019. Also, we explore the possible associations between spatiotemporal variability of the EPIs and global warming, ENSO, and local effects. Our resutls show marked rising trends in frequency and intensity of Yangtze precipitation extremes. Global warming tends to enhance the frequency and intensity of preciptation extremes over the YRB. The La Niña phase of ENSO could lead to an increase of precipitation extremes in the current year, but a decrease of precipitation extremes in the coming year. Local warming mainly exerts a reducing effect on precipitation extremes, which is likely associated with the significant decrease of relative humidity in the YRB. Our findings highlight the need for a systematic approach to investigate changes in precipitation extremes over the YRB.</p>

scholarly journals Distinguishing between Deep-Water Sediment Facies: Turbidites, Contourites and Hemipelagites

Geosciences ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 68 ◽  
Author(s):  
Dorrik Stow ◽  
Zeinab Smillie
Keyword(s):  
Deep Water ◽  
Large Scale ◽  
Tectonic Setting ◽  
Regional Scale ◽  
Small Scale ◽  
Physical Parameters ◽  
Sedimentary Characteristics ◽  
Natural Systems ◽  
Facies Types ◽  
Water Facies

The distinction between turbidites, contourites and hemipelagites in modern and ancient deep-water systems has long been a matter of controversy. This is partly because the processes themselves show a degree of overlap as part of a continuum, so that the deposit characteristics also overlap. In addition, the three facies types commonly occur within interbedded sequences of continental margin deposits. The nature of these end-member processes and their physical parameters are becoming much better known and are summarised here briefly. Good progress has also been made over the past decade in recognising differences between end-member facies in terms of their sedimentary structures, facies sequences, ichnofacies, sediment textures, composition and microfabric. These characteristics are summarised here in terms of standard facies models and the variations from these models that are typically encountered in natural systems. Nevertheless, it must be acknowledged that clear distinction is not always possible on the basis of sedimentary characteristics alone, and that uncertainties should be highlighted in any interpretation. A three-scale approach to distinction for all deep-water facies types should be attempted wherever possible, including large-scale (oceanographic and tectonic setting), regional-scale (architecture and association) and small-scale (sediment facies) observations.

scholarly journals Assessment of the heterogeneity of hydraulic properties in gravelly outwash plains: a regionally scaled sedimentological analysis in the Munich gravel plain, Germany

2020 ◽  
Vol 28 (8) ◽  
pp. 2657-2674
Author(s):  
Markus Theel ◽  
Peter Huggenberger ◽  
Kai Zosseder
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Large Scale ◽  
Regional Scale ◽  
Pumping Test ◽  
Small Scale ◽  
Significant Heterogeneity ◽  
Conductivity Distribution ◽  
Braided Rivers ◽  
Sedimentary Deposits

AbstractThe favorable overall conditions for the utilization of groundwater in fluvioglacial aquifers are impacted by significant heterogeneity in the hydraulic conductivity, which is related to small-scale facies changes. Knowledge of the spatial distribution of hydraulically relevant hydrofacies types (HF-types), derived by sedimentological analysis, helps to determine the hydraulic conductivity distribution and thus contribute to understanding the hydraulic dynamics in fluvioglacial aquifers. In particular, the HF-type “open framework gravel (OW)”, which occurs with the HF-type “bimodal gravel (BM)” in BM/OW couplings, has an intrinsically high hydraulic conductivity and significantly impacts hydrogeological challenges such as planning excavation-pit drainage or the prognosis of plumes. The present study investigates the properties and spatial occurrence of HF-types in fluvioglacial deposits at regional scale to derive spatial distribution trends of HF-types, by analyzing 12 gravel pits in the Munich gravel plain (southern Germany) as analogues for outwash plains. The results are compared to the reevaluation of 542 pumping tests. Analysis of the HF-types and the pumping test data shows similar small-scale heterogeneities of the hydraulic conductivity, superimposing large-scale trends. High-permeability BM/OW couples and their dependence on recognizable discharge types in the sedimentary deposits explain sharp-bounded small-scale heterogeneities in the hydraulic conductivity distribution from 9.1 × 10−3 to 2.2 × 10−4 m/s. It is also shown that high values of hydraulic conductivity can be interpolated on shorter distance compared to lower values. While the results of the HF-analysis can be transferred to other fluvioglacial settings (e.g. braided rivers), regional trends must be examined with respect to the surrounding topography.

scholarly journals A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns

2017 ◽  
Vol 30 (4) ◽  
pp. 1307-1326 ◽  
Author(s):  
Siyu Zhao ◽  
Yi Deng ◽  
Robert X. Black
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Warm Season ◽  
Base Function ◽  
Regional Patterns ◽  
Statistical Characterization ◽  
Climate System Model ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950–2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a “base function” for evaluating a climate model’s potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model’s skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4’s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.

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