Climate Models Permit Convection at Much Coarser Resolutions Than Previously Considered

2020 ◽  
Vol 33 (5) ◽  
pp. 1915-1933 ◽  
Author(s):  
Jesús Vergara-Temprado ◽  
Nikolina Ban ◽  
Davide Panosetti ◽  
Linda Schlemmer ◽  
Christoph Schär
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Model Performance ◽  
Shallow Convection ◽  
Grid Spacing ◽  
Radiative Balance ◽  
Model Biases ◽  
Convective Processes ◽  
Model Dynamics

AbstractThe “gray zone” of convection is defined as the range of horizontal grid-space resolutions at which convective processes are partially but not fully resolved explicitly by the model dynamics (typically estimated from a few kilometers to a few hundred meters). The representation of convection at these scales is challenging, as both parameterizing convective processes or relying on the model dynamics to resolve them might cause systematic model biases. Here, a regional climate model over a large European domain is used to study model biases when either using parameterizations of deep and shallow convection or representing convection explicitly. For this purpose, year-long simulations at horizontal resolutions between 50- and 2.2-km grid spacing are performed and evaluated with datasets of precipitation, surface temperature, and top-of-the-atmosphere radiation over Europe. While simulations with parameterized convection seem more favorable than using explicit convection at around 50-km resolution, at higher resolutions (grid spacing ≤ 25 km) models tend to perform similarly or even better for certain model skills when deep convection is turned off. At these finer scales, the representation of deep convection has a larger effect in model performance than changes in resolution when looking at hourly precipitation statistics and the representation of the diurnal cycle, especially over nonorographic regions. The shortwave net radiative balance at the top of the atmosphere is the variable most strongly affected by resolution changes, due to the better representation of cloud dynamical processes at higher resolutions. These results suggest that an explicit representation of convection may be beneficial in representing some aspects of climate over Europe at much coarser resolutions than previously thought, thereby reducing some of the uncertainties derived from parameterizing deep convection.

Metrics and Diagnostics for Precipitation-Related Processes in Climate Model Short-Range Hindcasts

2013 ◽  
Vol 26 (5) ◽  
pp. 1516-1534 ◽  
Author(s):  
H.-Y. Ma ◽  
S. Xie ◽  
J. S. Boyle ◽  
S. A. Klein ◽  
Y. Zhang
Keyword(s):  
Short Range ◽  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Regional Analysis ◽  
Atmospheric Model ◽  
Department Of Energy ◽  
Temperature Fields ◽  
Lower Probability ◽  
Model Biases

Abstract In this study, several metrics and diagnostics are proposed and implemented to systematically explore and diagnose climate model biases in short-range hindcasts and quantify how fast hindcast biases approach to climate biases with an emphasis on tropical precipitation and associated moist processes. A series of 6-day hindcasts with NCAR and the U.S. Department of Energy Community Atmosphere Model, version 4 (CAM4) and version 5 (CAM5), were performed and initialized with ECMWF operational analysis every day at 0000 UTC during the Year of Tropical Convection (YOTC). An Atmospheric Model Intercomparison Project (AMIP) type of ensemble climate simulations was also conducted for the same period. The analyses indicate that initial drifts in precipitation and associated moisture processes (“fast processes”) can be identified in the hindcasts, and the biases share great resemblance to those in the climate runs. Comparing to Tropical Rainfall Measuring Mission (TRMM) observations, model hindcasts produce too high a probability of low- to intermediate-intensity precipitation at daily time scales during northern summers, which is consistent with too frequently triggered convection by its deep convection scheme. For intense precipitation events (>25 mm day−1), however, the model produces a much lower probability partially because the model requires a much higher column relative humidity than observations to produce similar precipitation intensity as indicated by the proposed diagnostics. Regional analysis on precipitation bias in the hindcasts is also performed for two selected locations where most contemporary climate models show the same sign of bias. Based on moist static energy diagnostics, the results suggest that the biases in the moisture and temperature fields near the surface and in the lower and middle troposphere are primarily responsible for precipitation biases. These analyses demonstrate the usefulness of these metrics and diagnostics to diagnose climate model biases.

Evaluation of frontal precipitation in CMIP6 models

2020 ◽  
Author(s):  
Jennifer Catto ◽  
Matthew Priestley
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Model Development ◽  
Model Performance ◽  
Weather System ◽  
Hourly Precipitation ◽  
Model Biases ◽  
Atmospheric Fronts ◽  
Precipitation Estimates

<p>Process-based evaluation of precipitation is key to understanding climate model biases. It is vital to ensure that precipitation is produced in the model due to the correct mechanisms (or weather system). Atmospheric fronts have been shown to be responsible for a large proportion of total and extreme precipitation in the mid-latitudes. Therefore, representation of precipitation associated with fronts in climate models needs to be tested.</p><p>We applied objective front identification to the historical simulations from the CMIP6 archive and linked them with their 6-hourly precipitation accumulations. We compared the model outputs to the results from observationally constrained datasets. The fronts were identified from ERA5 and linked to precipitation estimates from sources including ERA5, and satellite products. This allows the precipitation errors to be decomposed into components associated with the frequency and intensity of frontal and non-frontal precipitation.</p><p>The diagnostics from the analysis have been made into metrics which could be used to evaluate model performance and aid in focussing future model development.</p>

scholarly journals Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing

2021 ◽  
Vol 14 (7) ◽  
pp. 4617-4639
Author(s):  
Christian Zeman ◽  
Nils P. Wedi ◽  
Peter D. Dueben ◽  
Nikolina Ban ◽  
Christoph Schär
Keyword(s):  
Diurnal Cycle ◽  
Climate Models ◽  
Deep Convection ◽  
Model Intercomparison ◽  
Grid Spacing ◽  
Step Size ◽  
Time Step ◽  
Time Step Size ◽  
Convective Processes ◽  
Hydrostatic Approximation

Abstract. The increase in computing power and recent model developments allow for the use of global kilometer-scale weather and climate models for routine forecasts. At these scales, deep convective processes can be partially resolved explicitly by the model dynamics. Next to horizontal resolution, other aspects such as the applied numerical methods, the use of the hydrostatic approximation, and time step size are factors that might influence a model's ability to resolve deep convective processes. In order to improve our understanding of the role of these factors, a model intercomparison between the nonhydrostatic COSMO model and the hydrostatic Integrated Forecast System (IFS) from ECMWF has been conducted. Both models have been run with different spatial and temporal resolutions in order to simulate 2 summer days over Europe with strong convection. The results are analyzed with a focus on vertical wind speed and precipitation. Results show that even at around 3 km horizontal grid spacing the effect of the hydrostatic approximation seems to be negligible. However, time step proves to be an important factor for deep convective processes, with a reduced time step generally allowing for higher updraft velocities and thus more energy in vertical velocity spectra, in particular for shorter wavelengths. A shorter time step is also causing an earlier onset and peak of the diurnal cycle. Furthermore, the amount of horizontal diffusion plays a crucial role for deep convection with more diffusion generally leading to larger convective cells and higher precipitation intensities. The study also shows that for both models the parameterization of deep convection leads to lower updraft and precipitation intensities and biases in the diurnal cycle with a precipitation peak which is too early.

scholarly journals Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing

2021 ◽  
Author(s):  
Christian Zeman ◽  
Nils P. Wedi ◽  
Peter D. Dueben ◽  
Nikolina Ban ◽  
Christoph Schär
Keyword(s):  
Diurnal Cycle ◽  
Climate Models ◽  
Deep Convection ◽  
Horizontal Resolution ◽  
Model Intercomparison ◽  
Grid Spacing ◽  
Convective Processes ◽  
Hydrostatic Approximation ◽  
Velocity Spectra ◽  
Convective Cells

Abstract. The increase in computing power and recent model developments allow the use of global kilometer-scale weather and climate models for routine forecasts. At these scales, deep convective processes can be partially resolved explicitly by the model dynamics. Next to horizontal resolution, other aspects such as the applied numerical methods, the use of the hydrostatic approximation, and timestep size are factors that might influence a model's ability of resolving deep convective processes. In order to improve our understanding of the role of these factors, a model intercomparison between the nonhydrostatic COSMO model and the hydrostatic Integrated Forecast System (IFS) from ECMWF has been conducted. Both models have been run with different spatial and temporal resolutions in order to simulate two summer days over Europe with strong convection. The results are analyzed with focus on vertical wind speed and precipitation. Results show that even at around 3 km horizontal grid spacing the effect of the hydrostatic approximation seems to be negligible. However, timestep proves to be an important factor for deep convective processes, with a reduced timestep generally allowing for higher updraft velocities and thus more energy in vertical velocity spectra, in particular for smaller wavelengths. A shorter timestep is also causing an earlier onset and peak of the diurnal cycle. Furthermore, the amount of horizontal diffusion plays a crucial role for deep convection with more diffusion generally leading to larger convective cells and higher precipitation intensities. The study also shows that for both models the parameterization of deep convection leads to lower updraft and precipitation intensities and biases in the diurnal cycle with a precipitation peak which is too early.

scholarly journals GoAmazon2014/5 campaign points to deep-inflow approach to deep convection across scales

2018 ◽  
Vol 115 (18) ◽  
pp. 4577-4582 ◽  
Author(s):  
Kathleen A. Schiro ◽  
Fiaz Ahmed ◽  
Scott E. Giangrande ◽  
J. David Neelin
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Deep Convection ◽  
Potential Temperature ◽  
Strong Relationship ◽  
Field Campaign ◽  
Convective Systems ◽  
Tropical Oceans ◽  
Mesoscale Convective ◽  
Environmental Air

A substantial fraction of precipitation is associated with mesoscale convective systems (MCSs), which are currently poorly represented in climate models. Convective parameterizations are highly sensitive to the assumptions of an entraining plume model, in which high equivalent potential temperature air from the boundary layer is modified via turbulent entrainment. Here we show, using multiinstrument evidence from the Green Ocean Amazon field campaign (2014–2015; GoAmazon2014/5), that an empirically constrained weighting for inflow of environmental air based on radar wind profiler estimates of vertical velocity and mass flux yields a strong relationship between resulting buoyancy measures and precipitation statistics. This deep-inflow weighting has no free parameter for entrainment in the conventional sense, but to a leading approximation is simply a statement of the geometry of the inflow. The structure further suggests the weighting could consistently apply even for coherent inflow structures noted in field campaign studies for MCSs over tropical oceans. For radar precipitation retrievals averaged over climate model grid scales at the GoAmazon2014/5 site, the use of deep-inflow mixing yields a sharp increase in the probability and magnitude of precipitation with increasing buoyancy. Furthermore, this applies for both mesoscale and smaller-scale convection. Results from reanalysis and satellite data show that this holds more generally: Deep-inflow mixing yields a strong precipitation–buoyancy relation across the tropics. Deep-inflow mixing may thus circumvent inadequacies of current parameterizations while helping to bridge the gap toward representing mesoscale convection in climate models.

Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction

2018 ◽  
Vol 31 (16) ◽  
pp. 6591-6610 ◽  
Author(s):  
Martin Aleksandrov Ivanov ◽  
Jürg Luterbacher ◽  
Sven Kotlarski
Keyword(s):  
Climate Change ◽  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Daily Precipitation ◽  
Analytical Description ◽  
Analytical Theory ◽  
Climate Change Signal ◽  
Future Research ◽  
Model Biases

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

Scale-dependent analysis of climate model biases in relation to dynamics

2020 ◽  
Author(s):  
Katarina Kosovelj ◽  
Nedjeljka Žagar
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Physical Space ◽  
Single Mode ◽  
Kelvin Wave ◽  
Bias Analysis ◽  
Simultaneous Treatment ◽  
Model Biases ◽  
Bias Variance

<p>The assessment of climate model biases in an important part of their validation, in particular with respect to the application of the outputs of global models as lateral boundaries in regional climate models. The coupled nature of thermodynamics and circulation asks for their simultaneous treatment in the model bias analysis. This can be achieved by applying the normal-mode decomposition of model outputs and reanalysis that provides biases associated with the two dominant atmospheric regimes, the Rossby (or balanced) and inertia-gravity (or unbalanced) regime. The regime decomposition provides the spectrum of bias in terms of zonal wavenumbers, meridional modes and vertical modes. This can be especially useful in the tropics, where the Rossby and IG regimes are difficult to separate and biases in simulated circulation, just like the circulation itself, have global impacts. </p><p>The method is applied to the intermediate complexity climate model SPEEDY. Fifty-year long simulations  are performed in AMIP-mode with the prescribed SST. Biases are computed with respect to ERA-20C  upscaled to the resolution of SPEEDY (T30L8). We evaluate biases both in modal and physical space and study regional biases associated with the  balanced and unbalanced components of circulation. This work thus expands the results presented by Žagar et al. (2019, Clim. Dyn.) to the two regimes-related bias analysis..</p><p>The results show that the bias is strongly scale dependent, just like the simulated variability. The largest biases in SPEEDY are at planetary scales (waveumbers 0-3). Biases associated with the extratropical Rossby modes explain more than the half of bias variance. The Rossby n=1 mode is a single mode with the largest bias variance in balanced circulation whereas the Kelvin wave contains the largest bias among the IG modes. These biases are shown to originate mostly in the stratosphere and the upper-troposphere westerlies in the Southern hemisphere. </p>

scholarly journals Assessment of Bias Assumptions for Climate Models

2014 ◽  
Vol 27 (17) ◽  
pp. 6799-6818 ◽  
Author(s):  
Christian Kerkhoff ◽  
Hans R. Künsch ◽  
Christoph Schär
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Climate Model ◽  
Spatial Scales ◽  
Regional Climate ◽  
Lower Troposphere ◽  
Regional Climate Models ◽  
Added Value ◽  
Model Biases ◽  
Interaction Component

Abstract Climate scenarios make implicit or explicit assumptions about the extrapolation of climate model biases from current to future time periods. Such assumptions are inevitable because of the lack of future observations. This manuscript reviews different bias assumptions found in the literature and provides measures to assess their validity. The authors explicitly separate climate change from multidecadal variability to systematically analyze climate model biases in seasonal and regional surface temperature averages, using global and regional climate models (GCMs and RCMs) from the Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project over Europe. For centennial time scales, it is found that a linear bias extrapolation for GCMs is best supported by the analysis: that is, it is generally not correct to assume that model biases are independent of the climate state. Results also show that RCMs behave markedly differently when forced with different drivers. RCM and GCM biases are not additive, and there is a significant interaction component in the bias of the RCM–GCM model chain that depends on both the RCM and GCM considered. This result questions previous studies that deduce biases (and ultimately projections) in RCM–GCM combinations from reanalysis-driven simulations. The authors suggest that the aforementioned interaction component derives from the refined RCM representation of dynamical and physical processes in the lower troposphere, which may nonlinearly depend upon the larger-scale circulation stemming from the driving GCM. The authors’ analyses also show that RCMs provide added value and that the combined RCM–GCM approach yields, in general, smaller biases in seasonal surface temperature and interannual variability, particularly in summer and even for spatial scales that are, in principle, well resolved by the GCMs.

scholarly journals On the potential use of glacier and permafrost observations for verification of climate models

1997 ◽  
Vol 25 ◽  
pp. 400-406 ◽  
Author(s):  
Martin Beniston ◽  
Wilfried Haeberli ◽  
Martin Hoelzle ◽  
Alan Taylor
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Regional Climate ◽  
Model Performance ◽  
Regional Climate Models ◽  
General Circulation Models ◽  
Model Verification ◽  
Dominant Feature

While the capability of global and regional climate models in reproducing current climate has significantly improved over the past few years, the confidence in model results for remote regions, or those where complex orography is a dominant feature, is still relatively low. This is, in part, linked to the lack of observational data for model verification and intercomparison purposes.Glacier and permafrost observations are directly related to past and present energy flux patterns at the Earth-atmosphere interface and could be used as a proxy for air temperature and precipitation, particularly of value in remote mountain regions and boreal and Arctic zones where instrumental climate records are sparse or non-existent. It is particularly important to verify climate-model performance in these regions, as this is where most general circulation models (GCMs) predict the greatest changes in air temperatures in a warmer global climate.Existing datasets from glacier and permafrost monitoring sites in remote and high altitudes are described in this paper; the data could be used in model-verification studies, as a means to improving model performance in these regions.

scholarly journals Hydroclimate variability in Scandinavia over the last millennium – insights from a climate model–proxy data comparison

2017 ◽  
Vol 13 (12) ◽  
pp. 1831-1850 ◽  
Author(s):  
Kristina Seftigen ◽  
Hugues Goosse ◽  
Francois Klein ◽  
Deliang Chen
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Model Performance ◽  
Circulation Model ◽  
Last Millennium ◽  
Atmospheric Variability ◽  
Out Of Sample ◽  
Hydroclimate Variability ◽  
Transient Simulations

Abstract. The integration of climate proxy information with general circulation model (GCM) results offers considerable potential for deriving greater understanding of the mechanisms underlying climate variability, as well as unique opportunities for out-of-sample evaluations of model performance. In this study, we combine insights from a new tree-ring hydroclimate reconstruction from Scandinavia with projections from a suite of forced transient simulations of the last millennium and historical intervals from the CMIP5 and PMIP3 archives. Model simulations and proxy reconstruction data are found to broadly agree on the modes of atmospheric variability that produce droughts–pluvials in the region. Despite these dynamical similarities, large differences between simulated and reconstructed hydroclimate time series remain. We find that the GCM-simulated multi-decadal and/or longer hydroclimate variability is systematically smaller than the proxy-based estimates, whereas the dominance of GCM-simulated high-frequency components of variability is not reflected in the proxy record. Furthermore, the paleoclimate evidence indicates in-phase coherencies between regional hydroclimate and temperature on decadal timescales, i.e., sustained wet periods have often been concurrent with warm periods and vice versa. The CMIP5–PMIP3 archive suggests, however, out-of-phase coherencies between the two variables in the last millennium. The lack of adequate understanding of mechanisms linking temperature and moisture supply on longer timescales has serious implications for attribution and prediction of regional hydroclimate changes. Our findings stress the need for further paleoclimate data–model intercomparison efforts to expand our understanding of the dynamics of hydroclimate variability and change, to enhance our ability to evaluate climate models, and to provide a more comprehensive view of future drought and pluvial risks.

Sign in / Sign up

Export Citation Format

Share Document