scholarly journals Lightning Variability in Dynamically Downscaled Simulations of Alaska’s Present and Future Summer Climate

2020 ◽  
Vol 59 (6) ◽  
pp. 1139-1152 ◽  
Author(s):  
Peter A. Bieniek ◽  
Uma S. Bhatt ◽  
Alison York ◽  
John E. Walsh ◽  
Rick Lader ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Atmospheric Stability ◽  
Weather Conditions ◽  
Geographic Region ◽  
Lightning Activity ◽  
Convective Precipitation ◽  
Lower Magnitude ◽  
June July

AbstractLightning is a key driver of wildfire activity in Alaska. Quantifying its historical variability and trends has been challenging because of changes in the observational network, but understanding historical and possible future changes in lightning activity is important for fire management planning. Dynamically downscaled reanalysis and global climate model (GCM) data were used to statistically assess lightning data in geographic zones used operationally by fire managers across Alaska. Convective precipitation was found to be a key predictor of weekly lightning activity through multiple regression analysis, along with additional atmospheric stability, moisture, and temperature predictor variables. Model-derived estimates of historical June–July lightning since 1979 showed increasing but lower-magnitude trends than the observed record, derived from the highly heterogeneous lightning sensor network, over the same period throughout interior Alaska. Two downscaled GCM projections estimate a doubling of lightning activity over the same June–July season and geographic region by the end of the twenty-first century. Such a substantial increase in lightning activity may have significant impacts on future wildfire activity in Alaska because of increased opportunities for ignitions, although the final outcome also depends on fire weather conditions and fuels.

scholarly journals The role of serial European windstorm clustering for extreme seasonal losses as determined from multi-centennial simulations of high-resolution global climate model data

2018 ◽  
Vol 18 (11) ◽  
pp. 2991-3006 ◽  
Author(s):  
Matthew D. K. Priestley ◽  
Helen F. Dacre ◽  
Len C. Shaffrey ◽  
Kevin I. Hodges ◽  
Joaquim G. Pinto
Keyword(s):  
High Resolution ◽  
Return Period ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Geographic Region ◽  
Near Surface ◽  
Short Period

Abstract. Extratropical cyclones are the most damaging natural hazard to affect western Europe. Serial clustering occurs when many intense cyclones affect one specific geographic region in a short period of time which can potentially lead to very large seasonal losses. Previous studies have shown that intense cyclones may be more likely to cluster than less intense cyclones. We revisit this topic using a high-resolution climate model with the aim to determine how important clustering is for windstorm-related losses. The role of windstorm clustering is investigated using a quantifiable metric (storm severity index, SSI) that is based on near-surface meteorological variables (10 m wind speed) and is a good proxy for losses. The SSI is used to convert a wind footprint into losses for individual windstorms or seasons. 918 years of a present-day ensemble of coupled climate model simulations from the High-Resolution Global Environment Model (HiGEM) are compared to ERA-Interim reanalysis. HiGEM is able to successfully reproduce the wintertime North Atlantic/European circulation, and represent the large-scale circulation associated with the serial clustering of European windstorms. We use two measures to identify any changes in the contribution of clustering to the seasonal windstorm loss as a function of return period. Above a return period of 3 years, the accumulated seasonal loss from HiGEM is up to 20 % larger than the accumulated seasonal loss from a set of random resamples of the HiGEM data. Seasonal losses are increased by 10 %–20 % relative to randomized seasonal losses at a return period of 200 years. The contribution of the single largest event in a season to the accumulated seasonal loss does not change with return period, generally ranging between 25 % and 50 %. Given the realistic dynamical representation of cyclone clustering in HiGEM, and comparable statistics to ERA-Interim, we conclude that our estimation of clustering and its dependence on the return period will be useful for informing the development of risk models for European windstorms, particularly for longer return periods.

scholarly journals An Investigation of the Connections among Convection, Clouds, and Climate Sensitivity in a Global Climate Model

2014 ◽  
Vol 27 (5) ◽  
pp. 1845-1862 ◽  
Author(s):  
Ming Zhao
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Convective Precipitation ◽  
Entrainment Rate ◽  
Cloud Radiative Forcing ◽  
Precipitation Efficiency ◽  
Perturbed Physics

Abstract This study explores connections between process-level modeling of convection and global climate model (GCM) simulated clouds and cloud feedback to global warming through a set of perturbed-physics and perturbed sea surface temperature experiments. A bulk diagnostic approach is constructed, and a set of variables is derived and demonstrated to be useful in understanding the simulated relationship. In particular, a novel bulk quantity, the convective precipitation efficiency or equivalently the convective detrainment efficiency, is proposed as a simple measure of the aggregated properties of parameterized convection important to the GCM simulated clouds. As the convective precipitation efficiency increases in the perturbed-physics experiments, both liquid and ice water path decrease, with low and middle cloud fractions diminishing at a faster rate than high cloud fractions. This asymmetry results in a large sensitivity of top-of-atmosphere net cloud radiative forcing to changes in convective precipitation efficiency in this limited set of models. For global warming experiments, intermodel variations in the response of cloud condensate, low cloud fraction, and total cloud radiative forcing are well explained by model variations in response to total precipitation (or detrainment) efficiency. Despite significant variability, all of the perturbed-physics models produce a sizable increase in precipitation efficiency to warming. A substantial fraction of the increase is due to its convective component, which depends on the parameterization of cumulus mixing and convective microphysical processes. The increase in convective precipitation efficiency and associated change in convective cloud height distribution owing to warming explains the increased cloud feedback and climate sensitivity in recently developed Geophysical Fluid Dynamics Laboratory GCMs. The results imply that a cumulus scheme using fractional removal of condensate for precipitation and inverse calculation of the entrainment rate tends to produce a lower climate sensitivity than a scheme using threshold removal for precipitation and the entrainment rate formulated inversely dependent on convective depth.

scholarly journals Role of the Convection Scheme in Modeling Initiation and Intensification of Tropical Depressions over the North Atlantic

2017 ◽  
Vol 145 (4) ◽  
pp. 1495-1509 ◽  
Author(s):  
J.-P. Duvel ◽  
S. J. Camargo ◽  
A. H. Sobel
Keyword(s):  
Relative Humidity ◽  
Climate Model ◽  
Early Stage ◽  
Global Climate ◽  
Global Climate Model ◽  
Convective Available Potential Energy ◽  
Convective Precipitation ◽  
Cloud Base ◽  
Entrainment Rate ◽  
Convection Scheme

Abstract The authors analyze how modifications of the convective scheme modify the initiation of tropical depression vortices (TDVs) and their intensification into stronger warm-cored tropical cyclone–like vortices (TCs) in global climate model (GCM) simulations. The model’s original convection scheme has entrainment and cloud-base mass flux closures based on moisture convergence. Two modifications are considered: one in which entrainment is dependent on relative humidity and another in which the closure is based on the convective available potential energy (CAPE). Compared to reanalysis, TDVs are more numerous and intense in all three simulations, probably as a result of excessive parameterized deep convection at the expense of convection detraining at midlevel. The relative humidity–dependent entrainment rate increases both TDV initiation and intensification relative to the control. This is because this entrainment rate is reduced in the moist center of the TDVs, giving more intense convective precipitation, and also because it generates a moister environment that may favor the development of early stage TDVs. The CAPE closure inhibits the parameterized convection in strong TDVs, thus limiting their development despite a slight increase in the resolved convection. However, the maximum intensity reached by TC-like TDVs is similar in the three simulations, showing the statistical character of these tendencies. The simulated TCs develop from TDVs with different dynamical origins than those observed. For instance, too many TDVs and TCs initiate near or over southern West Africa in the GCM, collocated with the maximum in easterly wave activity, whose characteristics are also dependent on the convection scheme considered.

scholarly journals Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics

2016 ◽  
Vol 29 (2) ◽  
pp. 543-560 ◽  
Author(s):  
Ming Zhao ◽  
J.-C. Golaz ◽  
I. M. Held ◽  
V. Ramaswamy ◽  
S.-J. Lin ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Convective Precipitation ◽  
Climate Projections ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Convection Parameterization ◽  
Comparable Quality

Abstract Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the intermodel spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, the authors use a developmental version of the next-generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the model ensemble from phase 5 of CMIP (CMIP5). The authors demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model’s convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere–land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear observational constraint that favors one version of the authors’ model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections.

The Sensitivity of the Tropical-Mean Radiation Budget

2005 ◽  
Vol 18 (16) ◽  
pp. 3189-3203 ◽  
Author(s):  
Amy C. Clement ◽  
Brian Soden
Keyword(s):  
Climate Model ◽  
Decadal Variability ◽  
Global Climate ◽  
Global Climate Model ◽  
Hadley Circulation ◽  
Radiation Budget ◽  
Convective Precipitation ◽  
Model Sensitivity ◽  
Precipitation Efficiency ◽  
Sensitivity Experiments

Abstract A key disagreement exists between global climate model (GCM) simulations and satellite observations of the decadal variability in the tropical-mean radiation budget. Measurements from the Earth Radiation Budget Experiment (ERBE) over the period 1984–2001 indicate a trend of increasing longwave emission and decreasing shortwave reflection that no GCM can currently reproduce. Motivated by these results, a series of model sensitivity experiments is performed to investigate hypotheses that have been advanced to explain this discrepancy. Specifically, the extent to which a strengthening of the Hadley circulation or a change in convective precipitation efficiency can alter the tropical-mean radiation budget is assessed. Results from both model sensitivity experiments and an empirical analysis of ERBE observations suggest that the tropical-mean radiation budget is remarkably insensitive to changes in the tropical circulation. The empirical estimate suggests that it would require at least a doubling in strength of the Hadley circulation in order to generate the observed decadal radiative flux changes. In contrast, rather small changes in a model’s convective precipitation efficiency can generate changes comparable to those observed, provided that the precipitation efficiency lies near the upper end of its possible range. If, however, the precipitation efficiency of tropical convective systems is more moderate, the model experiments suggest that the climate would be rather insensitive to changes in its value. Further observations are necessary to constrain the potential effects of microphysics on the top-of-atmosphere radiation budget.

scholarly journals Storylines of weather-induced crop failure events under climate change

2021 ◽  
Vol 12 (4) ◽  
pp. 1503-1527
Author(s):  
Henrique M. D. Goulart ◽  
Karin van der Wiel ◽  
Christian Folberth ◽  
Juraj Balkovic ◽  
Bart van den Hurk
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Crop Failure ◽  
Large Ensembles ◽  
Dry Conditions

Abstract. Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the midwestern US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present-day, pre-industrial +2 and 3 ∘C warming, respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure and construct analogues of these failure conditions in future climate settings. We find that crop failures in the midwestern US are linked to low precipitation levels, and high temperature and diurnal temperature range (DTR) levels during July and August. Results suggest soybean failures are likely to increase with climate change. With more frequent warm years due to global warming, the joint hot–dry conditions leading to crop failures become mostly dependent on precipitation levels, reducing the importance of the relative compound contribution. While event analogues of the 2012 season are rare and not expected to increase, impact analogues show a significant increase in occurrence frequency under global warming, but for different combinations of the meteorological drivers than experienced in 2012. This has implications for assessment of the drivers of extreme impact events.

scholarly journals Weather-induced crop failure events under climate change: a storyline approach

2021 ◽  
Author(s):  
Henrique M. D. Goulart ◽  
Karin van der Wiel ◽  
Christian Folberth ◽  
Juraj Balkovic ◽  
Bart van den Hurk
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Crop Failure ◽  
Large Ensembles ◽  
Meteorological Features

Abstract. Unfavourable weather is a common cause for crop failures all over the world. Whilst extreme weather conditions may cause extreme impacts, crop failure commonly is induced by the occurrence of multiple and combined anomalous meteorological drivers. For these cases, the explanation of conditions leading to crop failure is complex, as the links connecting weather and crop yield can be multiple and non-linear. Furthermore, climate change is likely to perturb the meteorological conditions, possibly altering the occurrences of crop failures or leading to unprecedented drivers of extreme impacts. The goal of this study is to identify important meteorological drivers that cause crop failures and to explore changes in crop failures due to global warming. For that, we focus on a historical failure event, the extreme low soybean production during the 2012 season in the Midwest US. We first train a random forest model to identify the most relevant meteorological drivers of historical crop failures and to predict crop failure probabilities. Second, we explore the influence of global warming on crop failures and on the structure of compound drivers. We use large ensembles from the EC-Earth global climate model, corresponding to present day, pre-industrial +2 °C and 3 °C warming respectively, to isolate the global warming component. Finally, we explore the meteorological conditions inductive for the 2012 crop failure, and construct analogues of these failure conditions in future climate settings. Unlike present-day conditions, future warming may increase the probability of crop failures resulting from univariate meteorological features, reducing the importance of compound failure drivers. Impact-analogues show a significant increase under global warming, with changes in the corresponding drivers. This has implications for risk assessment, as changing drivers of extreme impact events are highly relevant.

High bit rate ACTS experiments for performing global science - Keck Telescope and global climate model

1996 ◽  
Author(s):  
Larry Bergman ◽  
J. Gary ◽  
Burt Edelson ◽  
Neil Helm ◽  
Judith Cohen ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Bit Rate ◽  
Global Science ◽  
High Bit Rate

scholarly journals A comparison of ship and satellite measurements of cloud properties with global climate model simulations in the southeast Pacific stratus deck

2010 ◽  
Vol 10 (14) ◽  
pp. 6527-6536 ◽  
Author(s):  
M. A. Brunke ◽  
S. P. de Szoeke ◽  
P. Zuidema ◽  
X. Zeng
Keyword(s):  
Diurnal Cycle ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Cloud Fraction ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Southeast Pacific ◽  
Cloud Thickness

Abstract. Here, liquid water path (LWP), cloud fraction, cloud top height, and cloud base height retrieved by a suite of A-train satellite instruments (the CPR aboard CloudSat, CALIOP aboard CALIPSO, and MODIS aboard Aqua) are compared to ship observations from research cruises made in 2001 and 2003–2007 into the stratus/stratocumulus deck over the southeast Pacific Ocean. It is found that CloudSat radar-only LWP is generally too high over this region and the CloudSat/CALIPSO cloud bases are too low. This results in a relationship (LWP~h9) between CloudSat LWP and CALIPSO cloud thickness (h) that is very different from the adiabatic relationship (LWP~h2) from in situ observations. Such biases can be reduced if LWPs suspected to be contaminated by precipitation are eliminated, as determined by the maximum radar reflectivity Zmax>−15 dBZ in the apparent lower half of the cloud, and if cloud bases are determined based upon the adiabatically-determined cloud thickness (h~LWP1/2). Furthermore, comparing results from a global model (CAM3.1) to ship observations reveals that, while the simulated LWP is quite reasonable, the model cloud is too thick and too low, allowing the model to have LWPs that are almost independent of h. This model can also obtain a reasonable diurnal cycle in LWP and cloud fraction at a location roughly in the centre of this region (20° S, 85° W) but has an opposite diurnal cycle to those observed aboard ship at a location closer to the coast (20° S, 75° W). The diurnal cycle at the latter location is slightly improved in the newest version of the model (CAM4). However, the simulated clouds remain too thick and too low, as cloud bases are usually at or near the surface.

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