scholarly journals Assessment of the Sources of Variation in Changes of Precipitation Characteristics over the Rhine Basin Using a Linear Mixed-Effects Model

2015 ◽  
Vol 28 (17) ◽  
pp. 6903-6919 ◽  
Author(s):  
Martin Hanel ◽  
T. Adri Buishand
Keyword(s):  
Greenhouse Gas ◽  
Climate Models ◽  
Climate Model ◽  
Summer Precipitation ◽  
Mixed Effects ◽  
Natural Variability ◽  
Linear Mixed Effects ◽  
Rhine Basin ◽  
Precipitation Characteristics ◽  
Sources Of Variation

Abstract A linear mixed-effects (LME) model is developed to discriminate the sources of variation in the changes of several precipitation characteristics over the Rhine basin as projected by an ensemble of 191 global climate model (GCM) simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The uncertainty in climate change projections originates from natural internal variability, imperfect climate models, and the unpredictability of future greenhouse gas forcing. The LME model allows for the quantification of the contribution of these sources of uncertainty as well as the interaction between greenhouse gas forcing and climate model. In addition, dependence between climate models can be accounted for by using a two-level LME model in which the GCMs are grouped according to their atmospheric circulation model. Statistical models of varied complexity are assessed by the Akaike information criterion. More than 60% of the variance of the changes in mean summer precipitation and various quantiles of 5-day summer precipitation at the end of the twenty-first century can be explained by the climate model. Differences between climate models are also the main source of uncertainty for the changes in three drought characteristics in the summer half-year. In winter, the differences between GCMs are smaller, and natural variability explains a large proportion of the variance of the changes. Natural variability is also the main source of uncertainty for the changes in two indices of extreme precipitation. The contribution of the forcing scenario to the variance of the changes is generally less than 25%.

scholarly journals A Study on Future Projections of Precipitation Characteristics around Japan in Early Summer Combining GPM DPR Observation and CMIP5 Large-Scale Environments

2019 ◽  
Vol 32 (16) ◽  
pp. 5251-5274 ◽  
Author(s):  
Chie Yokoyama ◽  
Yukari N. Takayabu ◽  
Osamu Arakawa ◽  
Tomoaki Ose
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Summer Precipitation ◽  
Early Summer ◽  
The Sea Of Japan ◽  
Coupled Models ◽  
Environmental Indices ◽  
Precipitation Characteristics ◽  
Pacific Side

AbstractThis study estimates future changes in the early summer precipitation characteristics around Japan using changes in the large-scale environment, by combining Global Precipitation Measurement precipitation radar observations and phase 5 of the Coupled Models Intercomparison Project climate model large-scale projections. Analyzing satellite-based data, we first relate precipitation in three types of rain events (small, organized, and midlatitude), which are identified via their characteristics, to the large-scale environment. Two environmental fields are chosen to determine the large-scale conditions of the precipitation: the sea surface temperature and the midlevel large-scale vertical velocity. The former is related to the lower-tropospheric thermal instability, while the latter affects precipitation via moistening/drying of the midtroposphere. Consequently, favorable conditions differ between the three types in terms of these two environmental fields. Using these precipitation–environment relationships, we then reconstruct the precipitation distributions for each type with reference to the two environmental indices in climate models for the present and future climates. Future changes in the reconstructed precipitation are found to vary widely between the three types in association with the large-scale environment. In more than 90% of models, the region affected by organized-type precipitation will expand northward, leading to a substantial increase in this type of precipitation near Japan along the Sea of Japan, and in northern and eastern Japan on the Pacific side, where its present amount is relatively small. This result suggests an elevated risk of heavy rainfall in those regions because the maximum precipitation intensity is more intense in organized-type precipitation than in the other two types.

Dependence of benefits of convection permitting models on large-scale conditions

2020 ◽  
Author(s):  
Danijel Belusic ◽  
Petter Lind ◽  
Oskar Landgren ◽  
Dominic Matte ◽  
Rasmus Anker Pedersen ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Rapid Change ◽  
Winter Season ◽  
Storm Track ◽  
Summer Precipitation ◽  
Weather Conditions ◽  
Special Focus

<p>Current literature strongly indicates large benefits of convection permitting models for subdaily summer precipitation extremes. There has been less insight about other variables, seasons and weather conditions. We examine new climate simulations over the Nordic region, performed with the HCLIM38 regional climate model at both convection permitting and coarser scales, searching for benefits of using convection permitting resolutions. The Nordic climate is influenced by the North Atlantic storm track and characterised by large seasonal contrasts in temperature and precipitation. It is also in rapid change, most notably in the winter season when feedback processes involving retreating snow and ice lead to larger warming than in many other regions. This makes the area an ideal testbed for regional climate models. We explore the effects of higher resolution and better reproduction of convection on various aspects of the climate, such as snow in the mountains, coastal and other thermal circulations, convective storms and precipitation with a special focus on extreme events. We investigate how the benefits of convection permitting models change with different variables and seasons, and also their sensitivity to different circulation regimes.</p>

scholarly journals Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature

2020 ◽  
Vol 33 (23) ◽  
pp. 10383-10402
Author(s):  
Giuliana Pallotta ◽  
Benjamin D. Santer
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Statistical Significance ◽  
Coupled Model ◽  
Natural Variability ◽  
Long Term Memory ◽  
Tropospheric Temperature ◽  
Wide Range ◽  
Multimodel Ensembles ◽  
Model Versus

AbstractStudies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.

scholarly journals Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

2020 ◽  
Vol 24 (5) ◽  
pp. 2817-2839
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac ◽  
Masa Kageyama
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Natural Variability ◽  
Climate Signal ◽  
Lena River ◽  
River Catchment ◽  
Model Simulations ◽  
Non Parametric

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems, and the population's traditional livelihoods. In the Lena River catchment, eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as a PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scales. The method yields dissimilarities or emergence levels ranging from 0 % to 100 % and the direction of change as a continuous time series itself. First, we showcase the method's advantage over the Kolmogorov–Smirnov metric using a synthetic dataset that resembles signals observed in the utilized climate models. Then, we focus on the Lena River catchment, where significant environmental changes are already apparent. On average, the emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50 %–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools (last access: 19 May 2020).

scholarly journals Greenhouse Gas–Induced Changes in Summer Precipitation over Colorado in NARCCAP Regional Climate Models*

2013 ◽  
Vol 26 (21) ◽  
pp. 8690-8697 ◽  
Author(s):  
Michael A. Alexander ◽  
James D. Scott ◽  
Kelly Mahoney ◽  
Joseph Barsugli
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Summer Precipitation ◽  
Regional Climate Change ◽  
Regional Climate Models ◽  
Specific Humidity ◽  
The North ◽  
Climate Model Simulations ◽  
Climate Change Assessment

Abstract Precipitation changes between 32-yr periods in the late twentieth and mid-twenty-first centuries are investigated using regional climate model simulations provided by the North American Regional Climate Change Assessment Program (NARCCAP). The simulations generally indicate drier summers in the future over most of Colorado and the border regions of the adjoining states. The decrease in precipitation occurs despite an increase in the surface specific humidity. The domain-averaged decrease in daily summer precipitation occurs in all of the models from the 50th through the 95th percentile, but without a clear agreement on the sign of change for the most extreme (top 1% of) events.

scholarly journals Characteristics of precipitation extremes over the Nordic region: added value of convection-permitting modeling

2021 ◽  
Author(s):  
Erika Médus ◽  
Emma Dybro Thomassen ◽  
Danijel Belušić ◽  
Petter Lind ◽  
Peter Berg ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Heavy Precipitation ◽  
Added Value ◽  
Extreme Value Analysis ◽  
In Situ Observation ◽  
Value Analysis ◽  
Nordic Region ◽  
Precipitation Characteristics

Abstract. It is well established that using km scale grid resolution for simulations of weather systems in weather and climate models enhances their realism. This study explores heavy and extreme precipitation characteristics over the Nordic region generated by the regional climate model, HARMONIE-Climate (HCLIM). Two model setups of HCLIM are used: ERA-Interim driven HCLIM12 covering Europe at 12 km resolution with parameterized convection and HCLIM3 covering the Nordic region with 3 km resolution and explicit deep convection. The HCLIM simulations are evaluated against several gridded and in situ observation datasets for the warm season from April to September regarding their ability to reproduce sub-daily and daily heavy precipitation statistics across the Nordic region. Both model setups are able to capture the daily heavy precipitation characteristics in the analyzed region. At sub-daily scale, HCLIM3 clearly improves the statistics of occurrence of the most intense heavy precipitation events, as well as the timing and amplitude of the diurnal cycle of these events compared to its forcing HCLIM12. Extreme value analysis shows that HCLIM3 provides added value in capturing sub-daily return levels compared to HCLIM12, which fails to produce the most extreme events. The results indicate clear benefits of the convection-permitting model in simulating heavy and extreme precipitation in the present-day climate, therefore, offering a motivating way forward to investigate the climate change impacts in the region.

scholarly journals Space lidar observations constrain longwave cloud feedback

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Thibault Vaillant de Guélis ◽  
Hélène Chepfer ◽  
Rodrigo Guzman ◽  
Marine Bonazzola ◽  
David M. Winker ◽  
...  
Keyword(s):  
Greenhouse Gas Emissions ◽  
Greenhouse Gas ◽  
Climate Models ◽  
Climate Model ◽  
Atmospheric Science ◽  
Future Climate ◽  
Cloud Feedbacks ◽  
Active Sensor ◽  
Lidar Observations

Abstract Some of the most challenging questions in atmospheric science relate to how clouds will respond as the climate warms. On centennial scales, the response of clouds could either weaken or enhance the warming due to greenhouse gas emissions. Here we use space lidar observations to quantify changes in cloud altitude, cover, and opacity over the oceans between 2008 and 2014, together with a climate model with a lidar simulator to also simulate these changes in the present-day climate and in a future, warmer climate. We find that the longwave cloud altitude feedback, found to be robustly positive in simulations since the early climate models and backed up by physical explanations, is not the dominant longwave feedback term in the observations, although it is in the model we have used. These results suggest that the enhanced longwave warming due to clouds might be overestimated in climate models. These results highlight the importance of developing a long-term active sensor satellite record to reduce uncertainties in cloud feedbacks and prediction of future climate.

scholarly journals Changes in Surface Wind Speed over North America from CMIP5 Model Projections and Implications for Wind Energy

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sujay Kulkarni ◽  
Huei-Ping Huang
Keyword(s):  
Wind Speed ◽  
North America ◽  
Greenhouse Gas ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Current Estimate

The centennial trends in the surface wind speed over North America are deduced from global climate model simulations in the Climate Model Intercomparison Project—Phase 5 (CMIP5) archive. Using the 21st century simulations under the RCP 8.5 scenario of greenhouse gas emissions, 5–10 percent increases per century in the 10 m wind speed are found over Central and East-Central United States, the Californian Coast, and the South and East Coasts of the USA in winter. In summer, climate models projected decreases in the wind speed ranging from 5 to 10 percent per century over the same coastal regions. These projected changes in the surface wind speed are moderate and imply that the current estimate of wind power potential for North America based on present-day climatology will not be significantly changed by the greenhouse gas forcing in the coming decades.

scholarly journals Emerging climate signals in the Lena River catchment: a non-parametric statistical approach

2019 ◽  
Author(s):  
Eric Pohl ◽  
Christophe Grenier ◽  
Mathieu Vrac ◽  
Masa Kageyama
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Climate Models ◽  
Climate Model ◽  
Natural Variability ◽  
Climate Signal ◽  
Lena River ◽  
River Catchment ◽  
Model Simulations ◽  
Non Parametric

Abstract. Climate change has far-reaching implications in permafrost-underlain landscapes with respect to hydrology, ecosystems and the population’s traditional livelihoods. In the Lena River catchment, Eastern Siberia, changing climatic conditions and the associated impacts are already observed or expected. However, as climate change progresses the question remains as to how far we are along this track and when these changes will constitute a significant emergence from natural variability. Here we present an approach to investigate temperature and precipitation time series from observational records, reanalysis, and an ensemble of 65 climate model simulations forced by the RCP8.5 emission scenario. We focus on the Lena River catchment, where significant environmental changes are already apparent. We developed a novel non-parametric statistical method to identify the time of emergence (ToE) of climate change signals, i.e. the time when a climate signal permanently exceeds its natural variability. The method is based on the Hellinger distance metric that measures the similarity of probability density functions (PDFs) roughly corresponding to their geometrical overlap. Natural variability is estimated as PDF for the earliest period common to all datasets used in the study (1901–1921) and is then compared to PDFs of target periods with moving windows of 21 years at annual and seasonal scale. The method yields dissimilarities or emergence levels ranging from 0 to 100 % and the direction of change as continuous time series itself. For the Lena River catchment, on average, emergence of temperature has a strong onset in the 1970s with a monotonic increase thereafter for validated reanalysis data. At the end of the reanalysis dataset (2004), temperature distributions have emerged by 50–60 %. Climate model projections suggest the same evolution on average and 90 % emergence by 2040. For precipitation the analysis is less conclusive because of high uncertainties in existing reanalysis datasets that also impede an evaluation of the climate models. Model projections suggest hardly any emergence by 2000 but a strong emergence thereafter, reaching 60 % by the end of the investigated period (2089). The presented ToE method provides more versatility than traditional parametric approaches and allows for a detailed temporal analysis of climate signal evolutions. An original strategy to select the most realistic model simulations based on the available observational data significantly reduces the uncertainties resulting from the spread in the 65 climate models used. The method comes as a toolbox available at https://github.com/pohleric/toe_tools.

Complexity in fitting Linear Mixed Models

2019 ◽  
Keyword(s):  
Mixed Models ◽  
Mixed Effects ◽  
Mixed Effects Models ◽  
Statistical Experimental Design ◽  
Experimental Conditions ◽  
Linear Mixed Effects Models ◽  
Linear Mixed Effects ◽  
Continuous Scale ◽  
Sources Of Variation ◽  
Blocking Factors

Linear mixed-effects models are increasingly used for the analysis of data from experiments in fields like psychology where several subjects are each exposed to each of several different items. In addition to a response, which here will be assumed to be on a continuous scale, such as a response time, a number of experimental conditions are systematically varied during the experiment. In the language of statistical experimental design the latter variables are called experimental factors whereas factors like Subject and Item are blocking factors. That is, these are known sources of variation that usually are not of interest by themselves but still should be accounted for when looking for systematic variation in the response.

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